#CALCULATING HWE

HWE for thal genotypes in all individuals

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    159
## 2 HET     222
## 3 HOM     101

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.861179 DF = 1 p-value = 0.1724888 D = -7.755187 f = 0.06530398

HWE for thal genotypes in 2010 malaria negative individuals

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    144
## 2 HET     203
## 3 HOM      95

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.959251 DF = 1 p-value = 0.1615934 D = -7.641968 f = 0.0700186

HWE for thal genotypes in individuals who also have g6pd_202_rtpcr genotypes

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    157
## 2 HET     222
## 3 HOM     100

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.533039 DF = 1 p-value = 0.2156569 D = -7.05428 f = 0.05975454

HWE for thal genotypes in 2010 malaria negative individuals who also have thal and g6pd_202_rtpcr genotypes

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    142
## 2 HET     203
## 3 HOM      94

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.607893 DF = 1 p-value = 0.2047883 D = -6.937927 f = 0.06398063

HWE for HbS genotypes in all individuals

## # A tibble: 3 x 2
##   sickle     n
##   <fct>  <int>
## 1 NORM     400
## 2 HET       79
## 3 HOM        2

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.4223818 DF = 1 p-value = 0.515751 D = 1.580561 f = -0.04168209

HWE for HbS genotypes in 2010 malaria negative individuals

## # A tibble: 3 x 2
##   sickle     n
##   <fct>  <int>
## 1 NORM     370
## 2 HET       69
## 3 HOM        2

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.126084 DF = 1 p-value = 0.7225273 D = 1.020975 f = -0.03049596

HWE for sickle genotypes in individuals who also have g6pd_202_rtpcr genotypes

## # A tibble: 3 x 2
##   sickle     n
##   <fct>  <int>
## 1 NORM     398
## 2 HET       78
## 3 HOM        2

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.3821579 DF = 1 p-value = 0.5364506 D = 1.516736 f = -0.04046436

HWE for sickle genotypes in 2010 malaria negative individuals who also have thal and g6pd_202_rtpcr genotypes

## # A tibble: 3 x 2
##   sickle     n
##   <fct>  <int>
## 1 NORM     368
## 2 HET       68
## 3 HOM        2

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.1021628 DF = 1 p-value = 0.7492495 D = 0.9589041 f = -0.02902156

HWE for g6pd_202_rtpcr genotypes in all individuals

##   A   B  AA  AB  BB 
## 185  59 148  75  12

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 1.030307 DF = 2 p-value = 0.597409 D = NA f = 0.04026791

HWE for g6pd_202_rtpcr genotypes in 2010 malaria negative individuals

##   A   B  AA  AB  BB 
## 170  54 136  69  10

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 0.889861 DF = 2 p-value = 0.6408688 D = NA f = 0.02237306

#DATA TABULATION

##Tabulating the distribution of gender, age among individuals of the different g6pd_202_rtpcr, thal and sickle genotypes before removal of sicklers and outliers

## Column 1 ['thal'] of item 2 is missing in item 1. Use fill=TRUE to fill with NA (NULL for list columns), or use.names=FALSE to ignore column names. use.names='check' (default from v1.12.2) emits this message and proceeds as if use.names=FALSE for  backwards compatibility. See news item 5 in v1.12.2 for options to control this message.
## Column 1 ['thal'] of item 2 is missing in item 1. Use fill=TRUE to fill with NA (NULL for list columns), or use.names=FALSE to ignore column names. use.names='check' (default from v1.12.2) emits this message and proceeds as if use.names=FALSE for  backwards compatibility. See news item 5 in v1.12.2 for options to control this message.
## Column 1 ['thal'] of item 2 is missing in item 1. Use fill=TRUE to fill with NA (NULL for list columns), or use.names=FALSE to ignore column names. use.names='check' (default from v1.12.2) emits this message and proceeds as if use.names=FALSE for  backwards compatibility. See news item 5 in v1.12.2 for options to control this message.
## Column 1 ['thal'] of item 2 is missing in item 1. Use fill=TRUE to fill with NA (NULL for list columns), or use.names=FALSE to ignore column names. use.names='check' (default from v1.12.2) emits this message and proceeds as if use.names=FALSE for  backwards compatibility. See news item 5 in v1.12.2 for options to control this message.

##Tabulating the distribution of individuals with different g6pd_202_rtpcr, thal and sickle genotypes among those with CBC data before removal of sicklers and outliers

HWE for sickle genotypes in all individuals

## # A tibble: 3 x 2
##   sickle     n
##   <fct>  <int>
## 1 NORM     400
## 2 HET       79
## 3 HOM        2
## Warning in homozyg(X): Genotypes are not labelled, default labels (AA, AB, BB)
## assumed.
## Warning in heterozyg(X): Genotypes are not labelled, default labels assumed.
## Warning in HWChisq(n): Expected counts below 5: chi-square approximation may be
## incorrect

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.4223818 DF = 1 p-value = 0.515751 D = 1.580561 f = -0.04168209

HWE for thal genotypes in all malaria negative individuals

## # A tibble: 3 x 2
##   sickle     n
##   <fct>  <int>
## 1 NORM     370
## 2 HET       69
## 3 HOM        2
## Warning in homozyg(X): Genotypes are not labelled, default labels (AA, AB, BB)
## assumed.
## Warning in heterozyg(X): Genotypes are not labelled, default labels assumed.
## Warning in HWChisq(n): Expected counts below 5: chi-square approximation may be
## incorrect

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 0.126084 DF = 1 p-value = 0.7225273 D = 1.020975 f = -0.03049596

Tabulating and removing 2 sicklers in the dataset before recalculating HWE but for g6pd_202_rtpcr and thal only

##     serial sickle
## 196 J709/5    HOM
## NA    <NA>   <NA>
## 461  N1183    HOM

##unadjusted analysis of g6pd202 on cbc indices before removal of those with incomplete genotypes {.tabset}

HWE for thal genotypes in all individuals

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    156
## 2 HET     221
## 3 HOM      99
## Warning in homozyg(X): Genotypes are not labelled, default labels (AA, AB, BB)
## assumed.
## Warning in heterozyg(X): Genotypes are not labelled, default labels assumed.

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.425149 DF = 1 p-value = 0.2325578 D = -6.793592 f = 0.05791955

HWE for thal genotypes in all malaria negative individuals

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    141
## 2 HET     202
## 3 HOM      93
## Warning in homozyg(X): Genotypes are not labelled, default labels (AA, AB, BB)
## assumed.

## Warning in homozyg(X): Genotypes are not labelled, default labels assumed.

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.49419 DF = 1 p-value = 0.2215675 D = -6.678899 f = 0.06202607

HWE for thal genotypes in individuals who also have thal and g6pd_202_rtpcr genotypes

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    156
## 2 HET     221
## 3 HOM      99
## Warning in homozyg(X): Genotypes are not labelled, default labels (AA, AB, BB)
## assumed.

## Warning in homozyg(X): Genotypes are not labelled, default labels assumed.

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.425149 DF = 1 p-value = 0.2325578 D = -6.793592 f = 0.05791955

HWE for thal genotypes in 2010 malaria negative individuals who also have thal and g6pd_202_rtpcr genotypes

## # A tibble: 3 x 2
##   thal      n
##   <fct> <int>
## 1 NORM    141
## 2 HET     202
## 3 HOM      93
## Warning in homozyg(X): Genotypes are not labelled, default labels (AA, AB, BB)
## assumed.

## Warning in homozyg(X): Genotypes are not labelled, default labels assumed.

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (autosomal) Chi2 = 1.49419 DF = 1 p-value = 0.2215675 D = -6.678899 f = 0.06202607

HWE for g6pd_202_rtpcr genotypes in all individuals

##   A   B  AA  AB  BB 
## 185  58 146  75  12

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 0.7459716 DF = 2 p-value = 0.688675 D = NA f = 0.03806457

HWE for g6pd_202_rtpcr genotypes in all malaria negative individuals

##   A   B  AA  AB  BB 
## 170  53 134  69  10

Chi-square test with continuity correction for Hardy-Weinberg equilibrium (X-chromosomal) Chi2 = 0.6154264 DF = 2 p-value = 0.7351261 D = NA f = 0.01997133

#EXPLORATORY DATA ANALYSIS

##Checking normality and equal variance of the phenotypes (non-transformed & transformed) in all and in malaria negative individuals

###2010 CBC histograms and boxplots before removal of sicklers

###2010 CBC normality and equal variance tests before removal of sicklers

###2010 CBC histograms and boxplots after removal of sicklers

###2010 CBC normality and equal variance tests after removal of sicklers

  rbc_2010 hgb_2010 mcv_2010 mch_2010 mchc_2010
p_shapiro 0.5046808 0.03516351 0.0007129853 0.0001631543 0.006952833
p_shapiro_log 0.002780564 2.213036e-05 1.63856e-07 1.061538e-08 0.0003660951
p_shapiro_sqrt 0.1693593 0.001789242 1.456029e-05 1.960701e-06 0.00169504
p_shapiro_inverse 7.536706e-09 8.371449e-10 1.343886e-11 1.962169e-13 1.353286e-05
p_var_sickle 0.9921741 0.9846741 0.4586994 0.4155749 0.155645
p_bartlett_g6pd 0.06063327 0.1451095 0.3171234 0.5136871 0.786732
p_bartlett_thal 0.05476978 0.1410895 1.304805e-06 3.675468e-08 0.3069541
p_fligner_g6pd 0.1364404 0.249633 0.1686052 0.2632893 0.8640181
p_fligner_thal 0.08708347 0.155958 0.002169804 2.690016e-05 0.7655256
p_shapiro_mn 0.4394745 0.09256748 0.002000697 0.0003419783 0.005413213
p_shapiro_log_mn 0.009100506 0.0001391937 6.128591e-07 3.478458e-08 0.0003418971
p_shapiro_sqrt_mn 0.2381383 0.007604258 4.82526e-05 5.209706e-06 0.001424406
p_shapiro_inverse_mn 7.944887e-08 8.581401e-09 6.079048e-11 9.218175e-13 1.642895e-05
p_var_sickle_mne 0.758997 0.8549812 0.6818469 0.6317379 0.3429152
p_bartlett_g6pd_mn 0.07875206 0.1367321 0.3733672 0.4841826 0.8604475
p_bartlett_thal_mne 0.06426269 0.1329222 1.580848e-06 6.391188e-08 0.1213792
p_fligner_g6pd_mn 0.1395444 0.1311293 0.2887646 0.3043104 0.8884057
p_fligner_thal_mn 0.0875653 0.1724153 0.001857777 4.320442e-05 0.5420789
p_shapiro_gn 0.4794388 0.07586737 0.0003581496 0.000117657 0.0085146
p_shapiro_log_gn 0.009409231 0.0001150672 5.141225e-07 7.617413e-08 0.0006156175
p_shapiro_sqrt_gn 0.2243972 0.005558282 1.714926e-05 4.284292e-06 0.002390263
p_shapiro_inverse_gn 1.600572e-07 1.230678e-08 2.820522e-10 1.225944e-11 3.443387e-05
p_var_sickle_gn 0.8368897 0.7699031 0.460513 0.3321916 0.08317237
p_bartlett_g6pd_gn 0.1930733 0.4798061 0.8347512 0.958035 0.9791681
p_bartlett_thal_gn 0.03917644 0.0585299 8.273476e-05 3.892797e-06 0.1221024
p_fligner_g6pd_gn 0.3134178 0.5757456 0.5834253 0.7465579 0.8503401
p_fligner_thal_gn 0.03176984 0.07090512 0.009850671 0.0005355212 0.4678615

#DATA TABULATION AFTER PRE-PROCESSING

##Tabulating the distribution of gender, age among individuals of the different g6pd_202_rtpcr, thal and sickle genotypes after removal of sicklers

##Tabulating the distribution of individuals with different g6pd_202_rtpcr, thal and sickle genotypes among those with CBC data after removal of sicklers

  rbc_2010 hgb_2010 mcv_2010 mch_2010 mchc_2010
G_all_NORM 331 331 331 331 331
G_all_HET 75 75 75 75 75
G_all_HOM.HEMI 70 70 70 70 70
G_all_Sum 476 476 476 476 476
T_all_NORM.1 156 156 156 156 156
T_all_HET.1 221 221 221 221 221
T_all_HOM 99 99 99 99 99
T_all_Sum.1 476 476 476 476 476
S_all_NORM.2 398 398 398 398 398
S_all_HET.2 78 78 78 78 78
S_all_Sum.2 476 476 476 476 476
G_all_g6pd_NORM1 331 331 331 331 331
G_all_g6pd_HET1 75 75 75 75 75
G_all_g6pd_HOM.HEMI1 70 70 70 70 70
G_all_g6pd_Sum1 476 476 476 476 476
T_all_g6pd_NORM.11 156 156 156 156 156
T_all_g6pd_HET.11 221 221 221 221 221
T_all_g6pd_HOM1 99 99 99 99 99
T_all_g6pd_Sum.11 476 476 476 476 476
S_all_g6pd_NORM.21 398 398 398 398 398
S_all_g6pd_HET.21 78 78 78 78 78
S_all_g6pd_Sum.21 476 476 476 476 476
G_mn_NORM2 304 304 304 304 304
G_mn_HET2 69 69 69 69 69
G_mn_HOM.HEMI2 63 63 63 63 63
G_mn_Sum2 436 436 436 436 436
T_mn_NORM.12 141 141 141 141 141
T_mn_HET.12 202 202 202 202 202
T_mn_HOM2 93 93 93 93 93
T_mn_Sum.12 436 436 436 436 436
S_mn_NORM.22 368 368 368 368 368
S_mn_HET.22 68 68 68 68 68
S_mn_Sum.22 436 436 436 436 436
G_mn_g6pd_NORM3 304 304 304 304 304
G_mn_g6pd_HET3 69 69 69 69 69
G_mn_g6pd_HOM.HEMI3 63 63 63 63 63
G_mn_g6pd_Sum3 436 436 436 436 436
T_mn_g6pd_NORM.13 141 141 141 141 141
T_mn_g6pd_HET.13 202 202 202 202 202
T_mn_g6pd_HOM3 93 93 93 93 93
T_mn_g6pd_Sum.13 436 436 436 436 436
S_mn_g6pd_NORM.23 368 368 368 368 368
S_mn_g6pd_HET.23 68 68 68 68 68
S_mn_g6pd_Sum.23 436 436 436 436 436

##Tabulations

Results of Hypothesis Test

Alternative Hypothesis:

Test Name: Shapiro-Wilk normality test

Data: pgd_genopheno_01042018$age_at_collection_years_2010

Test Statistic: W = 0.978256

P-value: 1.532054e-06

Results of Hypothesis Test

Alternative Hypothesis:

Test Name: Shapiro-Wilk normality test

Data: pgd_genopheno_01042018$u_rcc

Test Statistic: W = 0.9797794

P-value: 1.5957e-05

Results of Hypothesis Test

Alternative Hypothesis:

Test Name: Shapiro-Wilk normality test

Data: pgd_genopheno_01042018$u_ghb3

Test Statistic: W = 0.9864331

P-value: 0.0006673935

NORM (N=331) HET (N=75) HOM/HEMI (N=70) Total (N=476) p value
sex < 0.001
   FEMALE 146 (62.66%) 75 (32.19%) 12 (5.15%) 233 (100.00%)
   MALE 185 (76.13%) 0 (0.00%) 58 (23.87%) 243 (100.00%)
malaria_status 0.916
   no_malaria 264 (69.84%) 58 (15.34%) 56 (14.81%) 378 (100.00%)
   assymptomatic_malaria 40 (68.97%) 11 (18.97%) 7 (12.07%) 58 (100.00%)
   uncomplicated_malaria 27 (67.50%) 6 (15.00%) 7 (17.50%) 40 (100.00%)
rbc_2010 < 0.001
   meanCI 4.73 (4.67, 4.79) 4.52 (4.42, 4.62) 4.42 (4.31, 4.53) 4.65 (4.60, 4.70)
NORM (N=331) HET (N=75) HOM/HEMI (N=70) Total (N=476) p value
age_at_collection_years_2010 0.320
   Median 7.81 7.07 8.18 7.82
   IQR 4.68 4.92 4.41 4.78
   Range 0.73 - 13.71 1.02 - 14.01 1.32 - 13.65 0.73 - 14.01
   Median (MAD) 7.81 (2.39) 7.07 (2.50) 8.18 (2.41) 7.82 (2.42)
   Median (Q1, Q3) 7.81 (5.35, 10.03) 7.07 (4.81, 9.73) 8.18 (6.36, 10.77) 7.82 (5.34, 10.12)
hgb_2010 0.706
   Median 11.20 11.40 11.30 11.30
   IQR 1.50 1.45 1.38 1.50
   Range 8.20 - 14.10 8.20 - 13.40 9.20 - 14.10 8.20 - 14.10
   Median (MAD) 11.20 (0.80) 11.40 (0.60) 11.30 (0.70) 11.30 (0.70)
   Median (Q1, Q3) 11.20 (10.50, 12.00) 11.40 (10.55, 12.00) 11.30 (10.70, 12.08) 11.30 (10.50, 12.00)
mcv_2010 < 0.001
   Median 73.00 77.00 80.00 75.00
   IQR 12.00 10.00 6.75 11.00
   Range 50.00 - 97.00 58.00 - 87.20 56.60 - 92.00 50.00 - 97.00
   Median (MAD) 73.00 (6.00) 77.00 (5.00) 80.00 (3.50) 75.00 (6.00)
   Median (Q1, Q3) 73.00 (66.00, 78.00) 77.00 (70.50, 80.50) 80.00 (77.00, 83.75) 75.00 (68.00, 79.00)
mch_2010 < 0.001
   Median 24.60 25.20 26.60 24.80
   IQR 4.50 4.00 3.07 4.73
   Range 15.30 - 35.00 17.80 - 30.50 17.30 - 30.80 15.30 - 35.00
   Median (MAD) 24.60 (2.30) 25.20 (2.00) 26.60 (1.35) 24.80 (2.20)
   Median (Q1, Q3) 24.60 (21.60, 26.10) 25.20 (23.05, 27.05) 26.60 (24.83, 27.90) 24.80 (21.90, 26.63)
mchc_2010 0.491
   Median 33.30 33.00 33.10 33.20
   IQR 1.40 1.35 1.37 1.50
   Range 29.20 - 36.60 30.40 - 35.00 30.60 - 35.20 29.20 - 36.60
   Median (MAD) 33.30 (0.70) 33.00 (0.70) 33.10 (0.70) 33.20 (0.70)
   Median (Q1, Q3) 33.30 (32.50, 33.90) 33.00 (32.45, 33.80) 33.10 (32.40, 33.77) 33.20 (32.40, 33.90)
u_rcc < 0.001
   N-Miss 29 6 28 63
   Median 168.30 124.77 18.44 155.00
   IQR 69.88 71.91 25.01 79.96
   Range 0.00 - 395.31 9.52 - 323.70 0.00 - 230.94 0.00 - 395.31
   Median (MAD) 168.30 (32.92) 124.77 (35.23) 18.44 (12.68) 155.00 (39.89)
   Median (Q1, Q3) 168.30 (139.55, 209.43) 124.77 (90.07, 161.99) 18.44 (9.59, 34.60) 155.00 (119.66, 199.62)
u_ghb3 < 0.001
   N-Miss 29 6 28 63
   Median 7.14 4.81 0.66 6.46
   IQR 3.34 3.32 1.27 3.57
   Range 0.00 - 17.50 0.33 - 12.80 0.00 - 8.18 0.00 - 17.50
   Median (MAD) 7.14 (1.57) 4.81 (1.44) 0.66 (0.44) 6.46 (1.86)
   Median (Q1, Q3) 7.14 (5.68, 9.02) 4.81 (3.52, 6.84) 0.66 (0.36, 1.63) 6.46 (4.81, 8.38)
NORM (N=398) HET (N=78) Total (N=476) p value
sex 0.652
   FEMALE 193 (82.83%) 40 (17.17%) 233 (100.00%)
   MALE 205 (84.36%) 38 (15.64%) 243 (100.00%)
malaria_status 0.283
   no_malaria 318 (84.13%) 60 (15.87%) 378 (100.00%)
   assymptomatic_malaria 50 (86.21%) 8 (13.79%) 58 (100.00%)
   uncomplicated_malaria 30 (75.00%) 10 (25.00%) 40 (100.00%)
rbc_2010 0.439
   meanCI 4.64 (4.59, 4.70) 4.69 (4.57, 4.81) 4.65 (4.60, 4.70)
NORM (N=398) HET (N=78) Total (N=476) p value
age_at_collection_years_2010 0.933
   Median 7.83 7.65 7.82
   IQR 4.81 4.74 4.78
   Range 0.73 - 14.01 0.79 - 13.49 0.73 - 14.01
   Median (MAD) 7.83 (2.41) 7.65 (2.40) 7.82 (2.42)
   Median (Q1, Q3) 7.83 (5.23, 10.04) 7.65 (5.49, 10.23) 7.82 (5.34, 10.12)
hgb_2010 0.634
   Median 11.30 11.25 11.30
   IQR 1.47 1.50 1.50
   Range 8.20 - 14.10 8.20 - 14.00 8.20 - 14.10
   Median (MAD) 11.30 (0.70) 11.25 (0.75) 11.30 (0.70)
   Median (Q1, Q3) 11.30 (10.53, 12.00) 11.25 (10.50, 12.00) 11.30 (10.50, 12.00)
mcv_2010 0.407
   Median 75.00 74.00 75.00
   IQR 11.00 10.75 11.00
   Range 50.00 - 97.00 53.00 - 92.00 50.00 - 97.00
   Median (MAD) 75.00 (6.00) 74.00 (6.00) 75.00 (6.00)
   Median (Q1, Q3) 75.00 (68.00, 79.00) 74.00 (67.25, 78.00) 75.00 (68.00, 79.00)
mch_2010 0.195
   Median 24.95 24.50 24.80
   IQR 4.77 4.43 4.73
   Range 15.30 - 35.00 16.10 - 32.00 15.30 - 35.00
   Median (MAD) 24.95 (2.25) 24.50 (2.10) 24.80 (2.20)
   Median (Q1, Q3) 24.95 (22.00, 26.77) 24.50 (21.82, 26.25) 24.80 (21.90, 26.63)
mchc_2010 0.057
   Median 33.25 33.05 33.20
   IQR 1.40 1.27 1.50
   Range 29.20 - 36.60 30.40 - 35.00 29.20 - 36.60
   Median (MAD) 33.25 (0.75) 33.05 (0.65) 33.20 (0.70)
   Median (Q1, Q3) 33.25 (32.50, 33.90) 33.05 (32.30, 33.57) 33.20 (32.40, 33.90)
u_rcc 0.394
   N-Miss 44 19 63
   Median 154.76 156.50 155.00
   IQR 75.95 81.03 79.96
   Range 0.00 - 395.31 3.40 - 379.91 0.00 - 395.31
   Median (MAD) 154.76 (37.28) 156.50 (51.37) 155.00 (39.89)
   Median (Q1, Q3) 154.76 (118.99, 194.94) 156.50 (130.15, 211.18) 155.00 (119.66, 199.62)
u_ghb3 0.351
   N-Miss 44 19 63
   Median 6.36 7.04 6.46
   IQR 3.60 3.96 3.57
   Range 0.00 - 17.50 0.14 - 16.17 0.00 - 17.50
   Median (MAD) 6.36 (1.84) 7.04 (2.11) 6.46 (1.86)
   Median (Q1, Q3) 6.36 (4.75, 8.35) 7.04 (5.08, 9.04) 6.46 (4.81, 8.38)
NORM (N=156) HET (N=221) HOM (N=99) Total (N=476) p value
sex 0.682
   FEMALE 72 (30.90%) 112 (48.07%) 49 (21.03%) 233 (100.00%)
   MALE 84 (34.57%) 109 (44.86%) 50 (20.58%) 243 (100.00%)
malaria_status 0.775
   no_malaria 125 (33.07%) 174 (46.03%) 79 (20.90%) 378 (100.00%)
   assymptomatic_malaria 16 (27.59%) 28 (48.28%) 14 (24.14%) 58 (100.00%)
   uncomplicated_malaria 15 (37.50%) 19 (47.50%) 6 (15.00%) 40 (100.00%)
rbc_2010 < 0.001
   meanCI 4.41 (4.34, 4.49) 4.61 (4.54, 4.67) 5.12 (5.04, 5.20) 4.65 (4.60, 4.70)
NORM (N=156) HET (N=221) HOM (N=99) Total (N=476) p value
age_at_collection_years_2010 0.334
   Median 7.39 7.81 8.66 7.82
   IQR 5.15 5.30 4.27 4.78
   Range 0.73 - 13.65 0.80 - 14.01 0.73 - 13.54 0.73 - 14.01
   Median (MAD) 7.39 (2.59) 7.81 (2.71) 8.66 (1.98) 7.82 (2.42)
   Median (Q1, Q3) 7.39 (4.72, 9.87) 7.81 (5.23, 10.53) 8.66 (5.67, 9.94) 7.82 (5.34, 10.12)
hgb_2010 < 0.001
   Median 11.50 11.30 11.00 11.30
   IQR 1.40 1.50 1.15 1.50
   Range 8.20 - 14.10 8.20 - 14.10 8.40 - 13.30 8.20 - 14.10
   Median (MAD) 11.50 (0.70) 11.30 (0.70) 11.00 (0.70) 11.30 (0.70)
   Median (Q1, Q3) 11.50 (10.80, 12.20) 11.30 (10.50, 12.00) 11.00 (10.25, 11.40) 11.30 (10.50, 12.00)
mcv_2010 < 0.001
   Median 79.00 75.00 66.00 75.00
   IQR 8.00 7.00 5.00 11.00
   Range 51.00 - 97.00 54.00 - 90.00 50.00 - 82.00 50.00 - 97.00
   Median (MAD) 79.00 (4.00) 75.00 (3.00) 66.00 (3.00) 75.00 (6.00)
   Median (Q1, Q3) 79.00 (75.00, 83.00) 75.00 (71.00, 78.00) 66.00 (63.00, 68.00) 75.00 (68.00, 79.00)
mch_2010 < 0.001
   Median 26.80 24.90 21.30 24.80
   IQR 3.02 2.60 1.40 4.73
   Range 15.80 - 35.00 16.50 - 30.00 15.30 - 28.10 15.30 - 35.00
   Median (MAD) 26.80 (1.50) 24.90 (1.30) 21.30 (0.80) 24.80 (2.20)
   Median (Q1, Q3) 26.80 (25.27, 28.30) 24.90 (23.60, 26.20) 21.30 (20.50, 21.90) 24.80 (21.90, 26.63)
mchc_2010 < 0.001
   Median 33.60 33.20 32.10 33.20
   IQR 1.10 1.20 1.15 1.50
   Range 30.40 - 36.60 29.20 - 35.30 30.40 - 34.40 29.20 - 36.60
   Median (MAD) 33.60 (0.60) 33.20 (0.60) 32.10 (0.60) 33.20 (0.70)
   Median (Q1, Q3) 33.60 (33.10, 34.20) 33.20 (32.60, 33.80) 32.10 (31.75, 32.90) 33.20 (32.40, 33.90)
u_rcc 0.757
   N-Miss 21 30 12 63
   Median 151.70 155.80 155.24 155.00
   IQR 81.24 74.01 85.98 79.96
   Range 0.00 - 394.00 0.00 - 395.31 0.00 - 292.58 0.00 - 395.31
   Median (MAD) 151.70 (38.01) 155.80 (37.33) 155.24 (44.93) 155.00 (39.89)
   Median (Q1, Q3) 151.70 (121.60, 202.84) 155.80 (117.67, 191.68) 155.24 (117.36, 203.33) 155.00 (119.66, 199.62)
u_ghb3 0.014
   N-Miss 21 30 12 63
   Median 5.96 6.32 7.31 6.46
   IQR 3.06 3.69 4.50 3.57
   Range 0.00 - 16.17 0.00 - 17.50 0.00 - 15.33 0.00 - 17.50
   Median (MAD) 5.96 (1.54) 6.32 (1.97) 7.31 (2.32) 6.46 (1.86)
   Median (Q1, Q3) 5.96 (4.75, 7.81) 6.32 (4.72, 8.42) 7.31 (5.19, 9.70) 6.46 (4.81, 8.38)
NORM.NORM (N=111) HET.NORM (N=143) HOM.NORM (N=77) NORM.HET (N=23) HET.HET (N=40) HOM.HET (N=12) NORM.HOM/HEMI (N=22) HET.HOM/HEMI (N=38) HOM.HOM/HEMI (N=10) Total (N=476) p value
sex < 0.0011
   FEMALE 44 (18.88%) 68 (29.18%) 34 (14.59%) 23 (9.87%) 40 (17.17%) 12 (5.15%) 5 (2.15%) 4 (1.72%) 3 (1.29%) 233 (100.00%)
   MALE 67 (27.57%) 75 (30.86%) 43 (17.70%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 17 (7.00%) 34 (13.99%) 7 (2.88%) 243 (100.00%)
malaria_status 0.9231
   no_malaria 88 (23.28%) 116 (30.69%) 60 (15.87%) 18 (4.76%) 29 (7.67%) 11 (2.91%) 19 (5.03%) 29 (7.67%) 8 (2.12%) 378 (100.00%)
   assymptomatic_malaria 12 (20.69%) 15 (25.86%) 13 (22.41%) 3 (5.17%) 8 (13.79%) 0 (0.00%) 1 (1.72%) 5 (8.62%) 1 (1.72%) 58 (100.00%)
   uncomplicated_malaria 11 (27.50%) 12 (30.00%) 4 (10.00%) 2 (5.00%) 3 (7.50%) 1 (2.50%) 2 (5.00%) 4 (10.00%) 1 (2.50%) 40 (100.00%)
rbc_2010 < 0.0012
   meanCI 4.45 (4.35, 4.54) 4.72 (4.64, 4.81) 5.15 (5.06, 5.24) 4.37 (4.19, 4.55) 4.44 (4.33, 4.55) 5.07 (4.79, 5.35) 4.30 (4.08, 4.51) 4.35 (4.22, 4.49) 4.94 (4.73, 5.15) 4.65 (4.60, 4.70)
  1. Pearson’s Chi-squared test
  2. Linear Model ANOVA
NORM.NORM (N=111) HET.NORM (N=143) HOM.NORM (N=77) NORM.HET (N=23) HET.HET (N=40) HOM.HET (N=12) NORM.HOM/HEMI (N=22) HET.HOM/HEMI (N=38) HOM.HOM/HEMI (N=10) Total (N=476) p value
age_at_collection_years_2010 0.3261
   Median 7.59 7.64 8.75 7.07 6.26 9.30 7.23 9.06 7.51 7.82
   IQR 5.29 4.94 3.92 4.24 5.30 2.70 4.67 4.29 1.62 4.78
   Range 0.73 - 13.49 0.80 - 13.71 0.73 - 13.54 1.02 - 11.53 1.05 - 14.01 4.05 - 12.39 1.51 - 13.65 1.32 - 13.09 4.01 - 10.64 0.73 - 14.01
   Median (MAD) 7.59 (2.45) 7.64 (2.53) 8.75 (2.27) 7.07 (2.40) 6.26 (2.92) 9.30 (1.48) 7.23 (2.44) 9.06 (2.13) 7.51 (0.97) 7.82 (2.42)
   Median (Q1, Q3) 7.59 (4.65, 9.94) 7.64 (5.35, 10.29) 8.75 (5.55, 9.48) 7.07 (4.68, 8.92) 6.26 (4.65, 9.95) 9.30 (7.80, 10.50) 7.23 (5.95, 10.62) 9.06 (6.89, 11.19) 7.51 (6.75, 8.38) 7.82 (5.34, 10.12)
hgb_2010 0.0031
   Median 11.40 11.30 10.80 11.60 11.30 11.35 11.50 11.30 10.40 11.30
   IQR 1.50 1.50 1.20 1.20 1.43 0.77 1.15 1.40 0.98 1.50
   Range 8.20 - 14.10 8.50 - 13.80 8.50 - 13.30 9.70 - 13.40 8.20 - 13.20 8.40 - 12.10 10.00 - 14.10 9.20 - 14.10 9.80 - 12.10 8.20 - 14.10
   Median (MAD) 11.40 (0.80) 11.30 (0.80) 10.80 (0.60) 11.60 (0.70) 11.30 (0.75) 11.35 (0.35) 11.50 (0.65) 11.30 (0.70) 10.40 (0.55) 11.30 (0.70)
   Median (Q1, Q3) 11.40 (10.70, 12.20) 11.30 (10.50, 12.00) 10.80 (10.20, 11.40) 11.60 (10.95, 12.15) 11.30 (10.35, 11.77) 11.35 (10.92, 11.70) 11.50 (11.03, 12.17) 11.30 (10.83, 12.23) 10.40 (10.08, 11.05) 11.30 (10.50, 12.00)
mcv_2010 < 0.0011
   Median 79.00 73.00 65.00 81.00 76.65 68.85 83.50 80.00 67.50 75.00
   IQR 7.65 6.00 5.00 8.50 6.25 3.00 8.75 3.95 3.75 11.00
   Range 51.00 - 97.00 54.00 - 85.00 50.00 - 82.00 59.00 - 87.20 58.00 - 83.70 61.00 - 79.00 56.60 - 92.00 63.00 - 90.00 65.00 - 73.00 50.00 - 97.00
   Median (MAD) 79.00 (4.00) 73.00 (3.00) 65.00 (3.00) 81.00 (4.00) 76.65 (3.35) 68.85 (1.00) 83.50 (4.35) 80.00 (2.00) 67.50 (1.50) 75.00 (6.00)
   Median (Q1, Q3) 79.00 (74.50, 82.15) 73.00 (70.00, 76.00) 65.00 (62.00, 67.00) 81.00 (75.50, 84.00) 76.65 (72.75, 79.00) 68.85 (66.00, 69.00) 83.50 (78.00, 86.75) 80.00 (78.00, 81.95) 67.50 (66.00, 69.75) 75.00 (68.00, 79.00)
mch_2010 < 0.0011
   Median 26.60 24.60 21.10 27.20 25.05 21.80 28.10 26.60 21.30 24.80
   IQR 3.25 2.55 1.50 3.10 2.45 1.12 3.07 2.22 1.47 4.73
   Range 15.80 - 35.00 16.50 - 28.60 15.30 - 28.10 19.90 - 30.50 17.80 - 28.70 18.80 - 27.20 17.30 - 30.80 19.60 - 30.00 20.10 - 22.80 15.30 - 35.00
   Median (MAD) 26.60 (1.60) 24.60 (1.00) 21.10 (0.70) 27.20 (1.40) 25.05 (1.55) 21.80 (0.65) 28.10 (1.75) 26.60 (1.20) 21.30 (0.55) 24.80 (2.20)
   Median (Q1, Q3) 26.60 (24.85, 28.10) 24.60 (22.95, 25.50) 21.10 (20.30, 21.80) 27.20 (25.25, 28.35) 25.05 (24.17, 26.63) 21.80 (21.32, 22.45) 28.10 (26.15, 29.22) 26.60 (25.50, 27.72) 21.30 (21.00, 22.47) 24.80 (21.90, 26.63)
mchc_2010 < 0.0011
   Median 33.60 33.30 32.40 33.70 32.90 31.95 33.50 33.15 31.55 33.20
   IQR 1.10 1.20 1.20 0.90 1.17 1.05 1.15 1.25 1.05 1.50
   Range 30.40 - 36.60 29.20 - 35.30 30.60 - 34.30 32.30 - 35.00 30.40 - 34.90 30.40 - 34.40 30.70 - 34.50 31.20 - 35.20 30.60 - 32.90 29.20 - 36.60
   Median (MAD) 33.60 (0.60) 33.30 (0.60) 32.40 (0.60) 33.70 (0.40) 32.90 (0.60) 31.95 (0.45) 33.50 (0.60) 33.15 (0.65) 31.55 (0.55) 33.20 (0.70)
   Median (Q1, Q3) 33.60 (33.15, 34.25) 33.30 (32.70, 33.90) 32.40 (31.90, 33.10) 33.70 (33.20, 34.10) 32.90 (32.48, 33.65) 31.95 (31.68, 32.73) 33.50 (33.02, 34.18) 33.15 (32.52, 33.77) 31.55 (30.95, 32.00) 33.20 (32.40, 33.90)
u_rcc < 0.0011
   N-Miss 11 10 8 0 4 2 10 16 2 63
   Median 171.41 166.41 168.53 140.72 126.61 96.28 14.41 22.61 10.27 155.00
   IQR 75.12 60.00 69.53 86.30 69.43 65.78 74.72 19.52 33.45 79.96
   Range 59.35 - 394.00 0.00 - 395.31 54.19 - 292.58 9.52 - 323.70 71.27 - 267.95 60.10 - 195.37 0.00 - 228.30 0.00 - 230.94 0.00 - 175.70 0.00 - 395.31
   Median (MAD) 171.41 (35.60) 166.41 (29.72) 168.53 (37.89) 140.72 (49.82) 126.61 (34.18) 96.28 (33.35) 14.41 (12.71) 22.61 (9.96) 10.27 (7.90) 155.00 (39.89)
   Median (Q1, Q3) 171.41 (137.66, 212.78) 166.41 (144.84, 204.84) 168.53 (140.11, 209.64) 140.72 (77.41, 163.70) 126.61 (96.43, 165.86) 96.28 (66.10, 131.88) 14.41 (5.59, 80.32) 22.61 (12.93, 32.44) 10.27 (4.99, 38.43) 155.00 (119.66, 199.62)
u_ghb3 < 0.0011
   N-Miss 11 10 8 0 4 2 10 16 2 63
   Median 6.57 7.13 7.96 4.93 4.91 4.34 0.51 0.84 0.47 6.46
   IQR 2.67 3.16 3.49 3.38 3.28 2.69 2.42 0.74 1.56 3.57
   Range 2.19 - 16.17 0.00 - 17.50 2.89 - 15.33 0.33 - 12.80 2.86 - 10.05 2.66 - 9.00 0.00 - 8.04 0.00 - 7.67 0.00 - 8.18 0.00 - 17.50
   Median (MAD) 6.57 (1.37) 7.13 (1.47) 7.96 (1.89) 4.93 (1.54) 4.91 (1.50) 4.34 (1.40) 0.51 (0.44) 0.84 (0.35) 0.47 (0.36) 6.46 (1.86)
   Median (Q1, Q3) 6.57 (5.32, 7.99) 7.13 (5.71, 8.87) 7.96 (6.56, 10.05) 4.93 (2.80, 6.18) 4.91 (3.75, 7.04) 4.34 (3.05, 5.74) 0.51 (0.29, 2.71) 0.84 (0.49, 1.23) 0.47 (0.23, 1.79) 6.46 (4.81, 8.38)
  1. Kruskal-Wallis rank sum test
NORM.NORM (N=279) HET.NORM (N=52) NORM.HET (N=61) HET.HET (N=14) NORM.HOM/HEMI (N=58) HET.HOM/HEMI (N=12) Total (N=476) p value
sex < 0.0011
   FEMALE 122 (52.36%) 24 (10.30%) 61 (26.18%) 14 (6.01%) 10 (4.29%) 2 (0.86%) 233 (100.00%)
   MALE 157 (64.61%) 28 (11.52%) 0 (0.00%) 0 (0.00%) 48 (19.75%) 10 (4.12%) 243 (100.00%)
malaria_status 0.4261
   no_malaria 222 (58.73%) 42 (11.11%) 49 (12.96%) 9 (2.38%) 47 (12.43%) 9 (2.38%) 378 (100.00%)
   assymptomatic_malaria 34 (58.62%) 6 (10.34%) 9 (15.52%) 2 (3.45%) 7 (12.07%) 0 (0.00%) 58 (100.00%)
   uncomplicated_malaria 23 (57.50%) 4 (10.00%) 3 (7.50%) 3 (7.50%) 4 (10.00%) 3 (7.50%) 40 (100.00%)
rbc_2010 < 0.0012
   meanCI 4.72 (4.66, 4.78) 4.78 (4.63, 4.93) 4.55 (4.43, 4.66) 4.40 (4.12, 4.68) 4.37 (4.25, 4.49) 4.66 (4.37, 4.96) 4.65 (4.60, 4.70)
  1. Pearson’s Chi-squared test
  2. Linear Model ANOVA
NORM.NORM (N=279) HET.NORM (N=52) NORM.HET (N=61) HET.HET (N=14) NORM.HOM/HEMI (N=58) HET.HOM/HEMI (N=12) Total (N=476) p value
age_at_collection_years_2010 0.4161
   Median 7.77 8.46 7.82 5.40 8.18 7.96 7.82
   IQR 4.92 4.30 4.59 5.15 4.54 4.54 4.78
   Range 0.73 - 13.71 0.79 - 13.49 1.05 - 14.01 1.02 - 10.88 1.32 - 13.65 2.81 - 12.76 0.73 - 14.01
   Median (MAD) 7.77 (2.47) 8.46 (2.37) 7.82 (2.39) 5.40 (2.04) 8.18 (2.34) 7.96 (2.74) 7.82 (2.42)
   Median (Q1, Q3) 7.77 (5.04, 9.96) 8.46 (6.08, 10.38) 7.82 (5.25, 9.84) 5.40 (3.84, 8.98) 8.18 (6.37, 10.91) 7.96 (6.13, 10.67) 7.82 (5.34, 10.12)
hgb_2010 0.4641
   Median 11.20 11.30 11.40 10.65 11.25 11.35 11.30
   IQR 1.50 1.53 1.10 1.42 1.55 0.52 1.50
   Range 8.20 - 14.10 8.40 - 14.00 8.30 - 13.40 8.20 - 12.30 9.20 - 14.10 10.10 - 13.10 8.20 - 14.10
   Median (MAD) 11.20 (0.80) 11.30 (0.80) 11.40 (0.60) 10.65 (0.85) 11.25 (0.80) 11.35 (0.30) 11.30 (0.70)
   Median (Q1, Q3) 11.20 (10.50, 12.00) 11.30 (10.50, 12.03) 11.40 (10.90, 12.00) 10.65 (10.22, 11.65) 11.25 (10.63, 12.17) 11.35 (11.03, 11.55) 11.30 (10.50, 12.00)
mcv_2010 < 0.0011
   Median 73.00 72.00 77.00 76.00 81.00 77.00 75.00
   IQR 12.00 10.25 9.00 10.75 7.75 6.00 11.00
   Range 50.00 - 97.00 53.00 - 92.00 58.00 - 87.20 59.00 - 85.00 56.60 - 92.00 66.00 - 81.00 50.00 - 97.00
   Median (MAD) 73.00 (6.00) 72.00 (5.50) 77.00 (5.00) 76.00 (5.50) 81.00 (4.00) 77.00 (3.00) 75.00 (6.00)
   Median (Q1, Q3) 73.00 (66.00, 78.00) 72.00 (66.00, 76.25) 77.00 (71.00, 80.00) 76.00 (70.25, 81.00) 81.00 (77.00, 84.75) 77.00 (72.50, 78.50) 75.00 (68.00, 79.00)
mch_2010 < 0.0011
   Median 24.60 24.05 25.30 24.85 26.70 25.05 24.80
   IQR 4.70 4.00 4.00 4.08 2.90 3.22 4.73
   Range 15.30 - 35.00 16.10 - 32.00 17.80 - 30.50 18.40 - 28.50 17.30 - 30.80 21.00 - 26.80 15.30 - 35.00
   Median (MAD) 24.60 (2.30) 24.05 (2.10) 25.30 (2.00) 24.85 (2.25) 26.70 (1.45) 25.05 (1.45) 24.80 (2.20)
   Median (Q1, Q3) 24.60 (21.50, 26.20) 24.05 (21.80, 25.80) 25.30 (23.10, 27.10) 24.85 (22.82, 26.90) 26.70 (25.40, 28.30) 25.05 (23.15, 26.37) 24.80 (21.90, 26.63)
mchc_2010 0.2511
   Median 33.30 33.20 33.10 32.95 33.25 32.55 33.20
   IQR 1.40 1.30 1.30 1.03 1.55 1.13 1.50
   Range 29.20 - 36.60 30.40 - 35.00 30.40 - 35.00 30.40 - 34.30 30.60 - 35.20 31.10 - 33.90 29.20 - 36.60
   Median (MAD) 33.30 (0.70) 33.20 (0.65) 33.10 (0.70) 32.95 (0.50) 33.25 (0.80) 32.55 (0.60) 33.20 (0.70)
   Median (Q1, Q3) 33.30 (32.50, 33.90) 33.20 (32.40, 33.70) 33.10 (32.50, 33.80) 32.95 (32.37, 33.40) 33.25 (32.40, 33.95) 32.55 (32.07, 33.20) 33.20 (32.40, 33.90)
u_rcc < 0.0011
   N-Miss 19 10 3 3 22 6 63
   Median 165.36 193.77 126.69 114.57 19.15 12.65 155.00
   IQR 64.63 70.08 84.95 50.00 31.22 16.32 79.96
   Range 0.00 - 395.31 80.94 - 379.91 9.52 - 323.70 38.65 - 148.08 0.00 - 230.94 3.40 - 175.70 0.00 - 395.31
   Median (MAD) 165.36 (32.74) 193.77 (38.55) 126.69 (39.05) 114.57 (26.15) 19.15 (12.81) 12.65 (7.44) 155.00 (39.89)
   Median (Q1, Q3) 165.36 (139.03, 203.67) 193.77 (153.76, 223.84) 126.69 (90.95, 175.90) 114.57 (89.63, 139.63) 19.15 (9.95, 41.16) 12.65 (8.37, 24.69) 155.00 (119.66, 199.62)
u_ghb3 < 0.0011
   N-Miss 19 10 3 3 22 6 63
   Median 7.02 7.83 4.84 4.26 0.70 0.48 6.46
   IQR 3.12 3.04 3.39 1.83 1.43 0.59 3.57
   Range 0.00 - 17.50 3.10 - 16.17 0.33 - 12.80 1.56 - 7.51 0.00 - 8.04 0.14 - 8.18 0.00 - 17.50
   Median (MAD) 7.02 (1.50) 7.83 (1.64) 4.84 (1.55) 4.26 (1.10) 0.70 (0.43) 0.48 (0.27) 6.46 (1.86)
   Median (Q1, Q3) 7.02 (5.58, 8.69) 7.83 (6.59, 9.63) 4.84 (3.53, 6.92) 4.26 (3.68, 5.51) 0.70 (0.37, 1.80) 0.48 (0.33, 0.93) 6.46 (4.81, 8.38)
  1. Kruskal-Wallis rank sum test

##2010 age and CBC summary (mean, sd, median) by rbc polymorphisms CBC summary in all individuals by rbc polymorphisms

## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
g6pd_202_rtpcr NORM HET HOM/HEMI
rbc_2010_mean 4.729728 4.520133 4.419000
hgb_2010_mean 11.23233 11.19333 11.39429
mcv_2010_mean 72.13233 75.20933 78.60857
mch_2010_mean 23.98369 24.91600 26.00143
mchc_2010_mean 33.17915 33.05600 33.01143
rbc_2010_se 0.03003609 0.05209883 0.05633608
hgb_2010_se 0.06709221 0.12985555 0.12153071
mcv_2010_se 0.4368500 0.8026043 0.8863813
mch_2010_se 0.1736594 0.3292527 0.3593616
mchc_2010_se 0.06010505 0.12446990 0.12237372
rbc_2010_sd 0.5464588 0.4511891 0.4713415
hgb_2010_sd 1.220636 1.124582 1.016799
mcv_2010_sd 7.947789 6.950757 7.415998
mch_2010_sd 3.159455 2.851412 3.006635
mchc_2010_sd 1.093515 1.077941 1.023852
rbc_2010_median 4.74 4.47 4.38
hgb_2010_median 11.2 11.4 11.3
mcv_2010_median 73 77 80
mch_2010_median 24.6 25.2 26.6
mchc_2010_median 33.3 33.0 33.1
rbc_2010_gmean 4.697179 4.498074 4.394707
hgb_2010_gmean 11.16485 11.13425 11.35008
mcv_2010_gmean 71.68336 74.87806 78.24335
mch_2010_gmean 23.76850 24.74600 25.81646
mchc_2010_gmean 33.16106 33.03846 32.99560
rbc_2010_lci 4.637095 4.396203 4.285636
hgb_2010_lci 11.03198 10.86773 11.11263
mcv_2010_lci 70.81329 73.24616 76.42225
mch_2010_lci 23.42121 24.07401 25.07062
mchc_2010_lci 33.04240 32.78936 32.75035
rbc_2010_uci 4.758042 4.602306 4.506553
hgb_2010_uci 11.29933 11.40729 11.59260
mcv_2010_uci 72.56413 76.54633 80.10784
mch_2010_uci 24.12093 25.43674 26.58449
mchc_2010_uci 33.28014 33.28946 33.24268
rbc_2010_Gsd 1.126443 1.104692 1.111155
hgb_2010_Gsd 1.117090 1.111044 1.092719
mcv_2010_Gsd 1.119567 1.100508 1.103808
mch_2010_Gsd 1.145829 1.127112 1.130824
mchc_2010_Gsd 1.033707 1.033442 1.031783
rbc_2010_min 2.92 3.41 3.62
hgb_2010_min 8.2 8.2 9.2
mcv_2010_min 50.0 58.0 56.6
mch_2010_min 15.3 17.8 17.3
mchc_2010_min 29.2 30.4 30.6
rbc_2010_max 6.14 5.66 5.84
hgb_2010_max 14.1 13.4 14.1
mcv_2010_max 97.0 87.2 92.0
mch_2010_max 35.0 30.5 30.8
mchc_2010_max 36.6 35.0 35.2
rbc_2010_IQR 0.7350001 0.6350000 0.7500001
hgb_2010_IQR 1.500 1.450 1.375
mcv_2010_IQR 12.00 10.00 6.75
mch_2010_IQR 4.500000 4.000000 3.074999
mchc_2010_IQR 1.400002 1.349998 1.374998
rbc_2010_q25 4.375 4.190 4.005
hgb_2010_q25 10.50 10.55 10.70
mcv_2010_q25 66.0 70.5 77.0
mch_2010_q25 21.600 23.050 24.825
mchc_2010_q25 32.50 32.45 32.40
rbc_2010_q75 5.110 4.825 4.755
hgb_2010_q75 12.000 12.000 12.075
mcv_2010_q75 78.00 80.50 83.75
mch_2010_q75 26.10 27.05 27.90
mchc_2010_q75 33.900 33.800 33.775
thal NORM HET HOM
rbc_2010_mean 4.414679 4.608416 5.118485
hgb_2010_mean 11.49423 11.25430 10.85556
mcv_2010_mean 78.03077 73.97466 65.63535
mch_2010_mean 26.26090 24.55611 21.25050
mchc_2010_mean 33.60256 33.15430 32.35556
rbc_2010_se 0.03971980 0.03252558 0.04023825
hgb_2010_se 0.09687952 0.07932879 0.10192158
mcv_2010_se 0.6434878 0.4324625 0.4972887
mch_2010_se 0.2510929 0.1703460 0.1797372
mchc_2010_se 0.07998629 0.06849868 0.08963177
rbc_2010_sd 0.4961002 0.4835275 0.4003655
hgb_2010_sd 1.210025 1.179307 1.014107
mcv_2010_sd 8.037161 6.429017 4.947960
mch_2010_sd 3.136149 2.532376 1.788362
mchc_2010_sd 0.9990284 1.0183060 0.8918248
rbc_2010_median 4.395 4.620 5.160
hgb_2010_median 11.5 11.3 11.0
mcv_2010_median 79 75 66
mch_2010_median 26.8 24.9 21.3
mchc_2010_median 33.6 33.2 32.1
rbc_2010_gmean 4.386572 4.583197 5.102486
hgb_2010_gmean 11.42877 11.19120 10.80758
mcv_2010_gmean 77.57766 73.68260 65.44965
mch_2010_gmean 26.05123 24.41524 21.17668
mchc_2010_gmean 33.58765 33.13849 32.34345
rbc_2010_lci 4.308037 4.519734 5.021449
hgb_2010_lci 11.23454 11.03333 10.60410
mcv_2010_lci 76.22501 72.80481 64.46624
mch_2010_lci 25.51541 24.06191 20.82515
mchc_2010_lci 33.42880 33.00221 32.16673
rbc_2010_uci 4.466538 4.647550 5.184831
hgb_2010_uci 11.62637 11.35133 11.01496
mcv_2010_uci 78.95431 74.57098 66.44805
mch_2010_uci 26.59831 24.77376 21.53414
mchc_2010_uci 33.74726 33.27533 32.52114
rbc_2010_Gsd 1.121005 1.110908 1.083578
hgb_2010_Gsd 1.114474 1.113119 1.099989
mcv_2010_Gsd 1.117638 1.094615 1.078862
mch_2010_Gsd 1.140428 1.116234 1.087550
mchc_2010_Gsd 1.030428 1.031573 1.027851
rbc_2010_min 2.92 3.36 4.00
hgb_2010_min 8.2 8.2 8.4
mcv_2010_min 51 54 50
mch_2010_min 15.8 16.5 15.3
mchc_2010_min 30.4 29.2 30.4
rbc_2010_max 5.86 6.14 6.04
hgb_2010_max 14.1 14.1 13.3
mcv_2010_max 97 90 82
mch_2010_max 35.0 30.0 28.1
mchc_2010_max 36.6 35.3 34.4
rbc_2010_IQR 0.6199999 0.5999999 0.5400000
hgb_2010_IQR 1.40 1.50 1.15
mcv_2010_IQR 8 7 5
mch_2010_IQR 3.025 2.600 1.400
mchc_2010_IQR 1.100002 1.200001 1.150002
rbc_2010_q25 4.110 4.300 4.845
hgb_2010_q25 10.80 10.50 10.25
mcv_2010_q25 75 71 63
mch_2010_q25 25.275 23.600 20.500
mchc_2010_q25 33.10 32.60 31.75
rbc_2010_q75 4.730 4.900 5.385
hgb_2010_q75 12.2 12.0 11.4
mcv_2010_q75 83 78 68
mch_2010_q75 28.3 26.2 21.9
mchc_2010_q75 34.2 33.8 32.9
sickle NORM HET
rbc_2010_mean 4.642588 4.693974
hgb_2010_mean 11.25704 11.21410
mcv_2010_mean 73.67764 73.01795
mch_2010_mean 24.49347 24.08974
mchc_2010_mean 33.17462 32.93333
rbc_2010_se 0.02684816 0.06048735
hgb_2010_se 0.05908877 0.13323412
mcv_2010_se 0.4087408 0.8609343
mch_2010_se 0.1608008 0.3364453
mchc_2010_se 0.05511182 0.10902753
rbc_2010_sd 0.5356190 0.5342098
hgb_2010_sd 1.178817 1.176692
mcv_2010_sd 8.154353 7.603566
mch_2010_sd 3.207965 2.971404
mchc_2010_sd 1.0994773 0.9629051
rbc_2010_median 4.650 4.735
hgb_2010_median 11.30 11.25
mcv_2010_median 75 74
mch_2010_median 24.95 24.50
mchc_2010_median 33.25 33.05
rbc_2010_gmean 4.611469 4.662481
hgb_2010_gmean 11.19391 11.15216
mcv_2010_gmean 73.21065 72.61669
mch_2010_gmean 24.27368 23.90224
mchc_2010_gmean 33.15629 32.91930
rbc_2010_lci 4.558738 4.539477
hgb_2010_lci 11.07644 10.88728
mcv_2010_lci 72.39146 70.89293
mch_2010_lci 23.94948 23.22589
mchc_2010_lci 33.04744 32.70131
rbc_2010_uci 4.664810 4.788817
hgb_2010_uci 11.31264 11.42348
mcv_2010_uci 74.03911 74.38237
mch_2010_uci 24.60227 24.59828
mchc_2010_uci 33.26550 33.13873
rbc_2010_Gsd 1.123788 1.125898
hgb_2010_Gsd 1.113003 1.112504
mcv_2010_Gsd 1.120963 1.112438
mch_2010_Gsd 1.146193 1.135771
mchc_2010_Gsd 1.033931 1.029905
rbc_2010_min 3.03 2.92
hgb_2010_min 8.2 8.2
mcv_2010_min 50 53
mch_2010_min 15.3 16.1
mchc_2010_min 29.2 30.4
rbc_2010_max 6.14 5.65
hgb_2010_max 14.1 14.0
mcv_2010_max 97 92
mch_2010_max 35 32
mchc_2010_max 36.6 35.0
rbc_2010_IQR 0.7349998 0.7975000
hgb_2010_IQR 1.475 1.500
mcv_2010_IQR 11.00 10.75
mch_2010_IQR 4.775 4.425
mchc_2010_IQR 1.400002 1.275000
rbc_2010_q25 4.2625 4.3075
hgb_2010_q25 10.525 10.500
mcv_2010_q25 68.00 67.25
mch_2010_q25 22.000 21.825
mchc_2010_q25 32.5 32.3
rbc_2010_q75 4.9975 5.1050
hgb_2010_q75 12 12
mcv_2010_q75 79 78
mch_2010_q75 26.775 26.250
mchc_2010_q75 33.900 33.575
g6pd_202_rtpcr NORM NORM NORM HET HET HET HOM/HEMI HOM/HEMI HOM/HEMI
thal NORM HET HOM NORM HET HOM NORM HET HOM
rbc_2010_mean 4.446757 4.723217 5.149740 4.372174 4.440500 5.069167 4.297273 4.353158 4.937000
hgb_2010_mean 11.46036 11.25944 10.85325 11.56957 11.01500 11.06667 11.58636 11.48684 10.62000
mcv_2010_mean 77.14414 72.10909 64.95065 79.02174 75.16250 68.05833 81.46818 79.74474 68.00000
mch_2010_mean 25.99459 23.96783 21.11429 26.62174 24.83750 21.90833 27.22727 26.47368 21.51000
mchc_2010_mean 33.63063 33.20000 32.48961 33.65652 32.98500 32.14167 33.40455 33.16053 31.58000
rbc_2010_se 0.04852200 0.04149057 0.04596277 0.08794976 0.05448588 0.12746631 0.10432049 0.06725686 0.09234052
hgb_2010_se 0.1246537 0.1002176 0.1177016 0.1871816 0.1920720 0.3208260 0.2030392 0.1689298 0.2365493
mcv_2010_se 0.7773953 0.4835641 0.5784866 1.4044812 0.9495524 1.2981606 1.6549988 0.8921157 0.7888106
mch_2010_se 0.3086544 0.1981014 0.2071576 0.5067121 0.4000270 0.5928180 0.6578590 0.3496937 0.3092822
mchc_2010_se 0.10126791 0.08773387 0.09587501 0.14755260 0.17080317 0.28774428 0.19434389 0.13300012 0.24212041
rbc_2010_sd 0.5112110 0.4961551 0.4033216 0.4217922 0.3445990 0.4415562 0.4893065 0.4145992 0.2920064
hgb_2010_sd 1.3133080 1.1984284 1.0328276 0.8976913 1.2147702 1.1113739 0.9523382 1.0413534 0.7480345
mcv_2010_sd 8.190368 5.782586 5.076199 6.735655 6.005497 4.496960 7.762633 5.499371 2.494438
mch_2010_sd 3.2518761 2.3689482 1.8178005 2.4301059 2.5299932 2.0535818 3.0856321 2.1556567 0.9780362
mchc_2010_sd 1.0669236 1.0491444 0.8412998 0.7076374 1.0802541 0.9967754 0.9115536 0.8198678 0.7656520
rbc_2010_median 4.460 4.710 5.170 4.310 4.465 5.170 4.195 4.350 4.845
hgb_2010_median 11.40 11.30 10.80 11.60 11.30 11.35 11.50 11.30 10.40
mcv_2010_median 79.00 73.00 65.00 81.00 76.65 68.85 83.50 80.00 67.50
mch_2010_median 26.60 24.60 21.10 27.20 25.05 21.80 28.10 26.60 21.30
mchc_2010_median 33.60 33.30 32.40 33.70 32.90 31.95 33.50 33.15 31.55
rbc_2010_gmean 4.416555 4.697094 5.133558 4.352605 4.427274 5.050786 4.272956 4.333848 4.929318
hgb_2010_gmean 11.38290 11.19479 10.80398 11.53594 10.94647 11.00984 11.54979 11.44091 10.59692
mcv_2010_gmean 76.67026 71.86497 64.75478 78.72398 74.91062 67.92553 81.06952 79.54821 67.95942
mch_2010_gmean 25.76794 23.84068 21.03725 26.50871 24.69952 21.82474 27.03226 26.38084 21.49013
mchc_2010_gmean 33.61369 33.18323 32.47892 33.64941 32.96753 32.12766 33.39241 33.15064 31.57168
rbc_2010_lci 4.318934 4.615370 5.040351 4.173513 4.317430 4.770030 4.075221 4.199581 4.726711
hgb_2010_lci 11.13221 10.99590 10.56977 11.15211 10.55211 10.27370 11.14205 11.10437 10.08583
mcv_2010_lci 75.03648 70.87418 63.61291 75.69256 72.91041 65.18011 77.39008 77.67683 66.21784
mch_2010_lci 25.11012 23.42631 20.62927 25.43084 23.84849 20.60813 25.54045 25.63780 20.80575
mchc_2010_lci 33.41211 33.00787 32.28931 33.34505 32.62064 31.50581 32.98404 32.88197 31.03047
rbc_2010_uci 4.516382 4.780265 5.228489 4.539382 4.539913 5.348066 4.480286 4.472408 5.140609
hgb_2010_uci 11.63924 11.39729 11.04337 11.93299 11.35557 11.79873 11.97244 11.78764 11.13390
mcv_2010_uci 78.33961 72.86961 65.91714 81.87682 76.96571 70.78659 84.92390 81.46468 69.74681
mch_2010_uci 26.44300 24.26238 21.45329 27.63227 25.58092 23.11319 28.61120 27.14541 22.19702
mchc_2010_uci 33.81649 33.35953 32.66963 33.95655 33.31811 32.76178 33.80583 33.42151 32.12233
rbc_2010_Gsd 1.126174 1.112019 1.084078 1.102040 1.081725 1.094188 1.112783 1.100480 1.060427
hgb_2010_Gsd 1.125685 1.114542 1.101373 1.081397 1.121568 1.115069 1.084437 1.095087 1.071544
mcv_2010_Gsd 1.121324 1.087608 1.081538 1.095058 1.088309 1.067089 1.110445 1.075115 1.036957
mch_2010_Gsd 1.147380 1.111895 1.090114 1.100753 1.115870 1.094477 1.136592 1.090810 1.046281
mchc_2010_Gsd 1.032494 1.032573 1.026131 1.021234 1.033628 1.031240 1.028141 1.025066 1.024466
rbc_2010_min 2.92 3.36 4.00 3.41 3.76 4.32 3.73 3.62 4.57
hgb_2010_min 8.2 8.5 8.5 9.7 8.2 8.4 10.0 9.2 9.8
mcv_2010_min 51.0 54.0 50.0 59.0 58.0 61.0 56.6 63.0 65.0
mch_2010_min 15.8 16.5 15.3 19.9 17.8 18.8 17.3 19.6 20.1
mchc_2010_min 30.4 29.2 30.6 32.3 30.4 30.4 30.7 31.2 30.6
rbc_2010_max 5.86 6.14 6.04 5.28 5.03 5.66 5.84 5.16 5.43
hgb_2010_max 14.1 13.8 13.3 13.4 13.2 12.1 14.1 14.1 12.1
mcv_2010_max 97.0 85.0 82.0 87.2 83.7 79.0 92.0 90.0 73.0
mch_2010_max 35.0 28.6 28.1 30.5 28.7 27.2 30.8 30.0 22.8
mchc_2010_max 36.6 35.3 34.3 35.0 34.9 34.4 34.5 35.2 32.9
rbc_2010_IQR 0.6099997 0.6650002 0.4400001 0.4800003 0.5400000 0.4425000 0.5825000 0.6674999 0.3799999
hgb_2010_IQR 1.5000000 1.5000000 1.1999998 1.2000003 1.4250002 0.7749999 1.1499999 1.4000001 0.9750001
mcv_2010_IQR 7.650002 6.000000 5.000000 8.500000 6.250000 3.000000 8.750000 3.950001 3.750000
mch_2010_IQR 3.250001 2.550000 1.500000 3.099999 2.450001 1.125000 3.074999 2.225000 1.474999
mchc_2010_IQR 1.1000004 1.2000008 1.1999989 0.8999996 1.1749983 1.0499997 1.1500006 1.2500000 1.0500002
rbc_2010_q25 4.1300 4.3900 4.9500 4.1600 4.1900 4.9175 3.9425 3.9900 4.7675
hgb_2010_q25 10.700 10.500 10.200 10.950 10.350 10.925 11.025 10.825 10.075
mcv_2010_q25 74.50 70.00 62.00 75.50 72.75 66.00 78.00 78.00 66.00
mch_2010_q25 24.850 22.950 20.300 25.250 24.175 21.325 26.150 25.500 21.000
mchc_2010_q25 33.150 32.700 31.900 33.200 32.475 31.675 33.025 32.525 30.950
rbc_2010_q75 4.7400 5.0550 5.3900 4.6400 4.7300 5.3600 4.5250 4.6575 5.1475
hgb_2010_q75 12.200 12.000 11.400 12.150 11.775 11.700 12.175 12.225 11.050
mcv_2010_q75 82.15 76.00 67.00 84.00 79.00 69.00 86.75 81.95 69.75
mch_2010_q75 28.100 25.500 21.800 28.350 26.625 22.450 29.225 27.725 22.475
mchc_2010_q75 34.250 33.900 33.100 34.100 33.650 32.725 34.175 33.775 32.000
g6pd_202_rtpcr NORM NORM HET HET HOM/HEMI HOM/HEMI
sickle NORM HET NORM HET NORM HET
rbc_2010_mean 4.720394 4.779808 4.547705 4.400000 4.368103 4.665000
hgb_2010_mean 11.21326 11.33462 11.31475 10.66429 11.40690 11.33333
mcv_2010_mean 72.12079 72.19423 75.40000 74.37857 79.35517 75.00000
mch_2010_mean 23.99677 23.91346 25.03115 24.41429 26.31724 24.47500
mchc_2010_mean 33.20215 33.05577 33.12295 32.76429 33.09655 32.60000
rbc_2010_se 0.03280459 0.07500955 0.05677122 0.12881114 0.06030446 0.13568290
hgb_2010_se 0.07314583 0.16934985 0.13837140 0.32047479 0.13976175 0.22540647
mcv_2010_se 0.4787121 1.0760081 0.8425825 2.3025113 0.9988201 1.5275252
mch_2010_se 0.1903450 0.4270460 0.3541290 0.8718338 0.4030295 0.6336672
mchc_2010_se 0.06676123 0.13449008 0.13903243 0.27508388 0.13775848 0.23257463
rbc_2010_sd 0.5479447 0.5409016 0.4433974 0.4819672 0.4592651 0.4700193
hgb_2010_sd 1.2217762 1.2211991 1.0807152 1.1991069 1.0643938 0.7808309
mcv_2010_sd 7.996069 7.759205 6.580780 8.615208 7.606788 5.291503
mch_2010_sd 3.179389 3.079472 2.765836 3.262103 3.069381 2.195087
mchc_2010_sd 1.1151324 0.9698218 1.0858780 1.0292696 1.0491373 0.8056621
rbc_2010_median 4.730 4.860 4.510 4.325 4.320 4.660
hgb_2010_median 11.20 11.30 11.40 10.65 11.25 11.35
mcv_2010_median 73 72 77 76 81 77
mch_2010_median 24.60 24.05 25.30 24.85 26.70 25.05
mchc_2010_median 33.30 33.20 33.10 32.95 33.25 32.55
rbc_2010_gmean 4.687877 4.747404 4.526581 4.375946 4.345049 4.642839
hgb_2010_gmean 11.14546 11.26950 11.26081 10.59919 11.35858 11.30911
mcv_2010_gmean 71.66537 71.77997 75.10655 73.89061 78.97028 74.82308
mch_2010_gmean 23.77869 23.71387 24.87311 24.19966 26.12370 24.38164
mchc_2010_gmean 33.18335 33.04168 33.10527 32.74895 33.07997 32.59085
rbc_2010_lci 4.622645 4.590725 4.415159 4.110785 4.228732 4.349829
hgb_2010_lci 11.000376 10.933043 10.975293 9.911541 11.084654 10.830317
mcv_2010_lci 70.71009 69.63609 73.39492 68.91394 76.89192 71.45874
mch_2010_lci 23.39756 22.85803 24.15032 22.31813 25.27365 22.99518
mchc_2010_lci 33.05149 32.77091 32.82636 32.15062 32.80226 32.08205
rbc_2010_uci 4.754030 4.909430 4.640815 4.658212 4.464565 4.955587
hgb_2010_uci 11.29245 11.61631 11.55375 11.33454 11.63927 11.80907
mcv_2010_uci 72.63356 73.98985 76.85809 79.22667 81.10482 78.34581
mch_2010_uci 24.16604 24.60175 25.61755 26.23982 27.00235 25.85169
mchc_2010_uci 33.31574 33.31469 33.38655 33.35841 33.36002 33.10772
rbc_2010_Gsd 1.126258 1.128111 1.102205 1.114340 1.108712 1.108049
hgb_2010_Gsd 1.117591 1.115020 1.105476 1.123192 1.097288 1.070456
mcv_2010_Gsd 1.120601 1.115069 1.094187 1.128359 1.106757 1.075094
mch_2010_Gsd 1.146949 1.141144 1.122036 1.150484 1.134070 1.096523
mchc_2010_Gsd 1.034363 1.029998 1.033587 1.032451 1.032582 1.025074
rbc_2010_min 3.03 2.92 3.41 3.76 3.62 3.76
hgb_2010_min 8.2 8.4 8.3 8.2 9.2 10.1
mcv_2010_min 50.0 53.0 58.0 59.0 56.6 66.0
mch_2010_min 15.3 16.1 17.8 18.4 17.3 21.0
mchc_2010_min 29.2 30.4 30.4 30.4 30.6 31.1
rbc_2010_max 6.14 5.65 5.66 5.28 5.84 5.43
hgb_2010_max 14.1 14.0 13.4 12.3 14.1 13.1
mcv_2010_max 97.0 92.0 87.2 85.0 92.0 81.0
mch_2010_max 35.0 32.0 30.5 28.5 30.8 26.8
mchc_2010_max 36.6 35.0 35.0 34.3 35.2 33.9
rbc_2010_IQR 0.7249999 0.8125000 0.5900002 0.6300000 0.7000000 0.5825000
hgb_2010_IQR 1.5000000 1.5250001 1.1000004 1.4249997 1.5499997 0.5249996
mcv_2010_IQR 12.00 10.25 9.00 10.75 7.75 6.00
mch_2010_IQR 4.700001 4.000001 4.000000 4.075000 2.900000 3.224999
mchc_2010_IQR 1.400002 1.299999 1.299999 1.025002 1.549998 1.125002
rbc_2010_q25 4.3750 4.3850 4.2200 4.1375 3.9825 4.3750
hgb_2010_q25 10.500 10.500 10.900 10.225 10.625 11.025
mcv_2010_q25 66.00 66.00 71.00 70.25 77.00 72.50
mch_2010_q25 21.500 21.800 23.100 22.825 25.400 23.150
mchc_2010_q25 32.500 32.400 32.500 32.375 32.400 32.075
rbc_2010_q75 5.1000 5.1975 4.8100 4.7675 4.6825 4.9575
hgb_2010_q75 12.000 12.025 12.000 11.650 12.175 11.550
mcv_2010_q75 78.00 76.25 80.00 81.00 84.75 78.50
mch_2010_q75 26.200 25.800 27.100 26.900 28.300 26.375
mchc_2010_q75 33.90 33.70 33.80 33.40 33.95 33.20

CBC and enzyme activity summary in all individuals by sex

sex FEMALE MALE
rbc_2010_mean 4.648155 4.653745
hgb_2010_mean 11.21202 11.28642
mcv_2010_mean 73.27983 73.84733
mch_2010_mean 24.30558 24.54403
mchc_2010_mean 33.09785 33.17078
rbc_2010_se 0.03317732 0.03607628
hgb_2010_se 0.08100970 0.07178178
mcv_2010_se 0.5026479 0.5405404
mch_2010_se 0.2000205 0.2105351
mchc_2010_se 0.07047862 0.06973386
rbc_2010_sd 0.5064298 0.5623736
hgb_2010_sd 1.236559 1.118967
mcv_2010_sd 7.672588 8.426190
mch_2010_sd 3.053180 3.281918
mchc_2010_sd 1.075809 1.087043
rbc_2010_median 4.66 4.66
hgb_2010_median 11.2 11.3
mcv_2010_median 74 75
mch_2010_median 24.8 24.9
mchc_2010_median 33.2 33.2
rbc_2010_gmean 4.620184 4.619413
hgb_2010_gmean 11.14200 11.23044
mcv_2010_gmean 72.86195 73.35451
mch_2010_gmean 24.10360 24.31723
mchc_2010_gmean 33.08028 33.15292
rbc_2010_lci 4.554521 4.548330
hgb_2010_lci 10.98028 11.08878
mcv_2010_lci 71.84788 72.27773
mch_2010_lci 23.69740 23.89621
mchc_2010_lci 32.94075 33.01513
rbc_2010_uci 4.686793 4.691606
hgb_2010_uci 11.30611 11.37391
mcv_2010_uci 73.89033 74.44733
mch_2010_uci 24.51676 24.74568
mchc_2010_uci 33.22040 33.29128
rbc_2010_Gsd 1.117280 1.130569
hgb_2010_Gsd 1.119941 1.105679
mcv_2010_Gsd 1.114698 1.124149
mch_2010_Gsd 1.140735 1.148224
mchc_2010_Gsd 1.033290 1.033508
rbc_2010_min 2.92 3.03
hgb_2010_min 8.2 8.3
mcv_2010_min 50 54
mch_2010_min 15.3 16.5
mchc_2010_min 30.4 29.2
rbc_2010_max 5.94 6.14
hgb_2010_max 14.1 14.1
mcv_2010_max 91 97
mch_2010_max 30.6 35.0
mchc_2010_max 35.4 36.6
rbc_2010_IQR 0.6900001 0.8250003
hgb_2010_IQR 1.5 1.4
mcv_2010_IQR 10.0 12.5
mch_2010_IQR 4.500000 4.849999
mchc_2010_IQR 1.399998 1.450001
rbc_2010_q25 4.310 4.225
hgb_2010_q25 10.5 10.6
mcv_2010_q25 68.0 67.5
mch_2010_q25 21.90 21.95
mchc_2010_q25 32.40 32.45
rbc_2010_q75 5.00 5.05
hgb_2010_q75 12 12
mcv_2010_q75 78 80
mch_2010_q75 26.4 26.8
mchc_2010_q75 33.8 33.9
sex FEMALE MALE
u_rcc_mean 162.2613 150.2016
u_ghb3_mean 6.820365 6.304721
u_rcc_se 4.269510 5.343687
u_ghb3_se 0.1940967 0.2328067
u_rcc_sd 61.57575 75.75984
u_ghb3_sd 2.799303 3.300605
u_rcc_median 157.1490 150.2077
u_ghb3_median 6.602785 6.310665
u_rcc_gmean 148.1259 115.2248
u_ghb3_gmean 6.154935 4.763225
u_rcc_lci 138.7247 101.0028
u_ghb3_lci 5.743836 4.162161
u_rcc_uci 158.1641 131.4494
u_ghb3_uci 6.595457 5.451090
u_rcc_Gsd 1.615549 2.578361
u_ghb3_Gsd 1.658129 2.637496
u_rcc_min 9.524391 3.398979
u_ghb3_min 0.3349411 0.1435462
u_rcc_max 379.9057 395.3092
u_ghb3_max 16.16551 17.50067
u_rcc_IQR 79.70515 74.80269
u_ghb3_IQR 3.482317 3.692795
u_rcc_q25 124.8328 117.5603
u_ghb3_q25 4.91479 4.72858
u_rcc_q75 204.538 192.363
u_ghb3_q75 8.397107 8.421375

enzyme activity summary in all individuals by rbc polymorphisms

g6pd_202_rtpcr NORM HET HOM/HEMI
u_rcc_mean 175.73129 130.79786 51.81299
u_ghb3_mean 7.462490 5.269736 1.950350
u_rcc_se 3.241782 7.181025 10.785468
u_ghb3_se 0.1491342 0.2918838 0.3949996
u_rcc_sd 56.24281 59.65007 67.35523
u_ghb3_sd 2.587382 2.424569 2.466772
u_rcc_median 168.5269 124.7674 19.6063
u_ghb3_median 7.1370615 4.8094102 0.7131003
u_rcc_gmean 166.81705 116.04375 24.98386
u_ghb3_gmean 7.020193 4.660080 0.973574
u_rcc_lci 160.66704 101.83377 16.92674
u_ghb3_lci 6.7405903 4.0790528 0.6671128
u_rcc_uci 173.20247 132.23660 36.87617
u_ghb3_uci 7.311394 5.323869 1.420819
u_rcc_Gsd 1.392592 1.722472 3.323606
u_ghb3_Gsd 1.430920 1.740797 3.209538
u_rcc_min 54.192707 9.524391 3.398979
u_ghb3_min 2.1923413 0.3349411 0.1435462
u_rcc_max 395.3092 323.7014 230.9428
u_ghb3_max 17.500669 12.804825 8.183425
u_rcc_IQR 69.98789 71.91367 36.55003
u_ghb3_IQR 3.344205 3.318816 1.465082
u_rcc_q25 139.64580 90.07294 10.59350
u_ghb3_q25 5.6888450 3.5210330 0.3970277
u_rcc_q75 209.63369 161.98661 47.14353
u_ghb3_q75 9.033050 6.839849 1.862110
thal NORM HET HOM
u_rcc_mean 161.8223 152.9865 155.1423
u_ghb3_mean 6.270190 6.399309 7.397789
u_rcc_se 6.416529 4.882728 7.022266
u_ghb3_se 0.2594050 0.2133727 0.3575672
u_rcc_sd 74.27670 67.12642 65.12181
u_ghb3_sd 3.002830 2.933390 3.315941
u_rcc_median 153.3215 156.5205 155.8709
u_ghb3_median 6.042139 6.392890 7.321995
u_rcc_gmean 133.9029 129.1865 130.1844
u_ghb3_gmean 5.151669 5.324334 6.134898
u_rcc_lci 116.8078 116.5362 110.1858
u_ghb3_lci 4.489929 4.780178 5.172777
u_rcc_uci 153.4999 143.2101 153.8128
u_ghb3_uci 5.910939 5.930433 7.275972
u_rcc_Gsd 2.224099 2.050749 2.176914
u_ghb3_Gsd 2.235829 2.119836 2.215841
u_rcc_min 3.398979 7.005303 4.747196
u_ghb3_min 0.1435462 0.2845517 0.2212455
u_rcc_max 394.0039 395.3092 292.5809
u_ghb3_max 16.16551 17.50067 15.32939
u_rcc_IQR 79.86424 73.25184 83.76700
u_ghb3_IQR 2.945448 3.641410 4.395576
u_rcc_q25 123.1510 118.4634 121.1082
u_ghb3_q25 4.866270 4.809410 5.332668
u_rcc_q75 203.0153 191.7152 204.8752
u_ghb3_q75 7.811718 8.450820 9.728243
sickle NORM HET
u_rcc_mean 155.6922 160.1460
u_ghb3_mean 6.530363 6.784029
u_rcc_se 3.641282 9.776166
u_ghb3_se 0.1613550 0.4340063
u_rcc_sd 68.12215 75.09216
u_ghb3_sd 3.018675 3.333666
u_rcc_median 155.1213 156.5034
u_ghb3_median 6.397652 7.042066
u_rcc_gmean 131.5413 127.3266
u_ghb3_gmean 5.443626 5.325591
u_rcc_lci 121.8160 100.6649
u_ghb3_lci 5.028729 4.188419
u_rcc_uci 142.0430 161.0498
u_ghb3_uci 5.892754 6.771509
u_rcc_Gsd 2.076361 2.463542
u_ghb3_Gsd 2.125703 2.513598
u_rcc_min 4.747196 3.398979
u_ghb3_min 0.1993038 0.1435462
u_rcc_max 395.3092 379.9057
u_ghb3_max 17.50067 16.16551
u_rcc_IQR 75.21142 81.03408
u_ghb3_IQR 3.523785 3.960841
u_rcc_q25 120.0910 130.1509
u_ghb3_q25 4.850011 5.078157
u_rcc_q75 195.3024 211.1850
u_ghb3_q75 8.373796 9.038998
g6pd_202_rtpcr NORM NORM NORM HET HET HET HOM/HEMI HOM/HEMI HOM/HEMI
thal NORM HET HOM NORM HET HOM NORM HET HOM
u_rcc_mean 179.89556 173.55839 173.85298 131.07626 138.08170 103.93570 61.80690 49.22864 43.86131
u_ghb3_mean 7.021658 7.344688 8.326735 4.949580 5.625747 4.724458 2.199936 1.783038 2.060080
u_rcc_se 6.259244 4.664460 6.233015 15.321296 8.767284 13.822562 23.871089 14.150910 23.869008
u_ghb3_se 0.2592179 0.2093933 0.3303890 0.5901362 0.3720397 0.6601840 0.8474558 0.4832788 1.1209772
u_rcc_sd 62.59244 53.59057 51.77531 73.47836 52.60370 43.71078 79.17144 64.84762 63.15146
u_ghb3_sd 2.592179 2.405746 2.744417 2.830194 2.232238 2.087685 2.810693 2.214662 2.965827
u_rcc_median 171.40792 166.55579 168.52694 140.72343 126.61452 96.27991 18.17770 25.61314 12.15665
u_ghb3_median 6.5660543 7.1328089 7.9634582 4.9312332 4.9086140 4.3398043 0.6291067 0.9587304 0.5314767
u_rcc_gmean 170.07431 165.11716 165.41643 106.20012 129.29057 96.41881 24.72818 27.58785 18.85839
u_ghb3_gmean 6.5931288 6.9410467 7.8572884 4.0070161 5.2282248 4.3586336 0.9248896 1.0374472 0.8721432
u_rcc_lci 159.080600 156.048128 152.719332 76.099606 114.330120 72.210766 9.058405 17.450938 5.252061
u_ghb3_lci 6.1425579 6.5348537 7.2152265 2.8696796 4.5898998 3.2376510 0.3542192 0.6625504 0.2402892
u_rcc_uci 181.82777 174.71326 179.16918 148.20662 146.20865 128.74240 67.50449 43.61309 67.71413
u_ghb3_uci 7.076750 7.372488 8.556485 5.595112 5.955323 5.867738 2.414948 1.624475 3.165492
u_rcc_Gsd 1.400431 1.388307 1.394387 2.161311 1.438277 1.498030 4.458645 2.734979 3.983722
u_ghb3_Gsd 1.428677 1.419384 1.425989 2.164133 1.469390 1.515297 4.172927 2.678136 4.030398
u_rcc_min 59.345262 58.223396 54.192707 9.524391 71.269935 60.101576 3.398979 7.005303 4.747196
u_ghb3_min 2.1923413 2.2775992 2.8938906 0.3349411 2.8566873 2.6573836 0.1435462 0.2845517 0.2212455
u_rcc_max 394.0039 395.3092 292.5809 323.7014 267.9529 195.3719 228.3035 230.9428 175.6983
u_ghb3_max 16.165506 17.500669 15.329394 12.804825 10.048232 9.000467 8.038586 7.669843 8.183425
u_rcc_IQR 75.11862 60.38923 69.53059 86.29817 69.43030 65.77771 93.43933 19.61359 43.75616
u_ghb3_IQR 2.6703436 3.1560542 3.4924922 3.3751712 3.2837715 2.6903808 3.1075602 0.7780939 2.1083933
u_rcc_q25 137.660144 144.972445 140.106062 77.406208 96.428559 66.099925 8.087665 13.232238 6.728672
u_ghb3_q25 5.3188241 5.7187727 6.5557390 2.8038944 3.7541915 3.0470145 0.3370247 0.4946822 0.3169058
u_rcc_q75 212.77876 205.36168 209.63665 163.70437 165.85886 131.87764 101.52700 32.84583 50.48483
u_ghb3_q75 7.989168 8.874827 10.048231 6.179066 7.037963 5.737395 3.444585 1.272776 2.425299
g6pd_202_rtpcr NORM NORM HET HET HOM/HEMI HOM/HEMI
sickle NORM HET NORM HET NORM HET
u_rcc_mean 173.24147 191.08522 135.20787 107.54505 53.95964 40.00639
u_ghb3_mean 7.358102 8.106213 5.421841 4.467729 1.982178 1.775297
u_rcc_se 3.472216 8.750118 8.146006 11.870397 11.876068 27.366569
u_ghb3_se 0.1598909 0.4031148 0.3296779 0.5393792 0.4142737 1.2881221
u_rcc_sd 55.88003 56.70724 62.03813 39.36965 68.22282 67.03413
u_ghb3_sd 2.573200 2.612483 2.510752 1.788919 2.379821 3.155242
u_rcc_median 166.03213 193.76543 126.69238 114.57294 25.61314 12.64523
u_ghb3_median 7.0337615 7.8311999 4.8374349 4.2557218 0.9587304 0.4842228
u_rcc_gmean 164.38458 182.63326 119.73766 98.37053 26.98372 16.35786
u_ghb3_gmean 6.9169097 7.6920766 4.7789884 4.0803357 1.0443686 0.6617628
u_rcc_lci 157.835885 165.650045 103.565769 70.825920 17.781513 3.918449
u_ghb3_lci 6.6186889 6.9265868 4.1161301 2.9598550 0.6997371 0.1522645
u_rcc_uci 171.20497 201.35768 138.43481 136.62740 40.94822 68.28712
u_ghb3_uci 7.228568 8.542164 5.548593 5.624985 1.558737 2.876114
u_rcc_Gsd 1.394086 1.367807 1.736428 1.630689 3.242142 3.902808
u_ghb3_Gsd 1.433588 1.399872 1.764516 1.612640 3.093759 4.055503
u_rcc_min 54.192707 80.937702 9.524391 38.647048 4.747196 3.398979
u_ghb3_min 2.1923413 3.1026119 0.3349411 1.5608904 0.1993038 0.1435462
u_rcc_max 395.3092 379.9057 323.7014 148.0792 230.9428 175.6983
u_ghb3_max 17.500669 16.165506 12.804825 7.514330 8.038586 8.183425
u_rcc_IQR 64.71086 70.08447 84.94576 49.99878 48.47420 16.32114
u_ghb3_IQR 3.1501264 3.0379678 3.3887858 1.8306944 1.5747720 0.5937863
u_rcc_q25 139.212907 153.759688 90.954696 89.633317 10.633050 8.369708
u_ghb3_q25 5.5784104 6.5915094 3.5283685 3.6772252 0.4019293 0.3327987
u_rcc_q75 203.92377 223.84416 175.90046 139.63210 59.10725 24.69085
u_ghb3_q75 8.728537 9.629477 6.917154 5.507920 1.976701 0.926585

Age summary in all individuals by rbc polymorphisms

g6pd_202_rtpcr NORM NORM NORM HET HET HET HOM/HEMI HOM/HEMI HOM/HEMI
thal NORM HET HOM NORM HET HOM NORM HET HOM
mean 7.325177 7.595225 7.788690 6.716342 7.151369 8.955353 7.527091 8.532616 7.447758
se 0.3190084 0.2752831 0.3630068 0.5989634 0.5713303 0.7347784 0.7448205 0.5293716 0.6509345
sd 3.360963 3.291907 3.185371 2.872528 3.613410 2.545347 3.493518 3.263265 2.058436
median 7.588809 7.635352 8.752738 7.073922 6.257615 9.302019 7.226129 9.060233 7.508984
gmean 6.229636 6.656560 6.848250 5.910239 6.107502 8.558408 6.409244 7.674356 7.165436
lci 5.501694 6.046777 5.985891 4.589391 5.007157 6.927208 4.775661 6.456101 5.767511
uci 7.053894 7.327836 7.834844 7.611232 7.449652 10.573718 8.601617 9.122493 8.902189
Gsd 1.935979 1.788179 1.809356 1.794849 1.861044 1.394882 1.941705 1.691979 1.354435
min 0.7268994 0.7965435 0.7268994 1.0191650 1.0520192 4.0547570 1.5054757 1.3184463 4.0136893
max 13.48785 13.71321 13.53833 11.52738 14.01369 12.38809 13.64904 13.09155 10.64083
IQR 5.291667 4.940537 3.922142 4.238450 5.303217 2.699435 4.667736 4.293036 1.624914
q25 4.645534 5.349162 5.554757 4.681725 4.650026 7.802019 5.947767 6.892112 6.754877
q75 9.937201 10.289699 9.476899 8.920175 9.953243 10.501454 10.615503 11.185147 8.379791
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10
g6pd_202_rtpcr NORM NORM HET HET HOM/HEMI HOM/HEMI
sickle NORM HET NORM HET NORM HET
mean 7.486841 7.886777 7.558533 6.208883 8.090857 7.920274
se 0.1979140 0.4418243 0.4232514 0.8360296 0.4248043 0.9130371
sd 3.305815 3.186040 3.305699 3.128136 3.235213 3.162853
median 7.774641 8.462440 7.821184 5.403747 8.177019 7.963809
gmean 6.495556 6.872437 6.654887 5.316352 7.172940 7.221224
lci 6.046919 5.760380 5.759202 3.670686 6.181868 5.324201
uci 6.977478 8.199179 7.689871 7.699815 8.322900 9.794159
Gsd 1.835424 1.885194 1.758413 1.899386 1.760358 1.615526
min 0.7268994 0.7883299 1.0520192 1.0191650 1.3184463 2.8129706
max 13.71321 13.48785 14.01369 10.87988 13.64904 12.75548
IQR 4.922142 4.302319 4.590007 5.147202 4.542351 4.536875
q25 5.042437 6.082349 5.246064 3.837440 6.370979 6.133299
q75 9.964579 10.384668 9.836071 8.984642 10.913330 10.670175
g6pd_202_rtpcr NORM HET HOM/HEMI
mean 7.549671 7.306598 8.061614
se 0.1806030 0.3805184 0.3825721
sd 3.285783 3.295386 3.200828
median 7.810233 7.073922 8.177019
gmean 6.553366 6.381690 7.181195
lci 6.134463 5.582296 6.300793
uci 7.000873 7.295558 8.184614
Gsd 1.842122 1.789044 1.730692
min 0.7268994 1.0191650 1.3184463
max 13.71321 14.01369 13.64904
IQR 4.676848 4.918036 4.409822
q25 5.349162 4.814938 6.364819
q75 10.026010 9.732974 10.774641
thal NORM HET HOM
mean 7.263888 7.676070 7.895666
se 0.2643119 0.2259335 0.3040482
sd 3.301254 3.358742 3.025242
median 7.389459 7.810233 8.659993
gmean 6.206310 6.715958 7.068068
lci 5.602761 6.217991 6.337948
uci 6.874876 7.253805 7.882296
Gsd 1.909551 1.788042 1.727497
min 0.7268994 0.7965435 0.7268994
max 13.64904 14.01369 13.53833
IQR 5.154646 5.303217 4.271133
q25 4.718686 5.229637 5.667437
q75 9.873332 10.532854 9.938569
sickle NORM HET
mean 7.585851 7.590770
se 0.1651191 0.3621115
sd 3.294116 3.198083
median 7.831366 7.653747
gmean 6.614659 6.613117
lci 6.238238 5.755551
uci 7.013795 7.598457
Gsd 1.812232 1.851537
min 0.7268994 0.7883299
max 14.01369 13.48785
IQR 4.805955 4.742556
q25 5.233744 5.491188
q75 10.03970 10.23374
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

Age summary in individuals with g6pd activity by rbc polymorphisms

g6pd_202_rtpcr NORM NORM NORM HET HET HET HOM/HEMI HOM/HEMI HOM/HEMI
thal NORM HET HOM NORM HET HOM NORM HET HOM
mean 7.408984 7.591440 7.847372 6.716342 7.307219 9.311294 6.557338 8.469775 7.407362
se 0.3409776 0.2850034 0.3941853 0.5989634 0.6090908 0.7006753 1.1183103 0.7690894 0.8036949
sd 3.409776 3.286820 3.274349 2.872528 3.654545 2.215730 3.873941 3.607349 2.273192
median 7.752139 7.718686 8.774641 7.073922 6.461841 9.302019 6.883984 9.060233 7.508984
min 0.7268994 0.7965435 0.7268994 1.0191650 1.0520192 5.4276181 1.5054757 1.3184463 4.0136893
max 13.48785 13.71321 13.53833 11.52738 14.01369 12.38809 11.25274 13.09155 10.64083
IQR 5.407084 4.922142 4.308693 4.238450 5.123631 2.549795 7.752353 4.819644 1.812500
q25 4.557110 5.307495 5.618925 4.681725 4.829612 8.274726 2.533924 6.416367 6.512705
q75 9.964194 10.229637 9.927618 8.920175 9.953243 10.824521 10.286277 11.236011 8.325205
g6pd_202_rtpcr thal n
NORM NORM 100
NORM HET 133
NORM HOM 69
HET NORM 23
HET HET 36
HET HOM 10
HOM/HEMI NORM 12
HOM/HEMI HET 22
HOM/HEMI HOM 8
g6pd_202_rtpcr NORM NORM HET HET HOM/HEMI HOM/HEMI
sickle NORM HET NORM HET NORM HET
mean 7.508235 8.092561 7.633582 6.172811 7.694901 7.877595
se 0.2060905 0.5063202 0.4339953 0.9454864 0.5862927 1.5342056
sd 3.323110 3.281330 3.305210 3.135823 3.517756 3.758021
median 7.806126 8.865589 7.851899 5.454997 7.651010 8.214579
min 0.7268994 0.7883299 1.0520192 1.0191650 1.3184463 2.8129706
max 13.71321 13.48785 14.01369 10.87988 13.09155 12.75548
IQR 4.986781 4.570029 4.350060 4.372262 5.542437 5.095654
q25 5.005176 6.124701 5.434463 4.091547 5.560832 5.231306
q75 9.991958 10.694730 9.784523 8.463809 11.103268 10.326959
g6pd_202_rtpcr sickle n
NORM NORM 260
NORM HET 42
HET NORM 58
HET HET 11
HOM/HEMI NORM 36
HOM/HEMI HET 6

CBC summary in malarianegative individuals

g6pd_202_rtpcr NORM HET HOM/HEMI
rbc_2010_mean 4.740000 4.533333 4.414127
hgb_2010_mean 11.21612 11.24783 11.39206
mcv_2010_mean 71.95099 75.29565 78.71270
mch_2010_mean 23.89967 24.97101 26.03016
mchc_2010_mean 33.14046 33.08696 33.00317
rbc_2010_se 0.03127269 0.05397841 0.05973257
hgb_2010_se 0.0704562 0.1272137 0.1328775
mcv_2010_se 0.4576118 0.8392453 0.9579524
mch_2010_se 0.1825739 0.3409849 0.3883060
mchc_2010_se 0.0630792 0.1273528 0.1327168
rbc_2010_sd 0.5452580 0.4483783 0.4741125
hgb_2010_sd 1.228446 1.056716 1.054683
mcv_2010_sd 7.978734 6.971295 7.603511
mch_2010_sd 3.183285 2.832433 3.082083
mchc_2010_sd 1.099823 1.057871 1.053407
rbc_2010_median 4.74 4.47 4.36
hgb_2010_median 11.2 11.4 11.3
mcv_2010_median 73 77 80
mch_2010_median 24.5 25.3 26.6
mchc_2010_median 33.2 33.1 33.1
rbc_2010_gmean 4.707758 4.511708 4.389640
hgb_2010_gmean 11.14776 11.19600 11.34458
mcv_2010_gmean 71.49740 74.96320 78.32899
mch_2010_gmean 23.68047 24.80413 25.83579
mchc_2010_gmean 33.12212 33.07013 32.98644
rbc_2010_lci 4.645321 4.406293 4.274133
hgb_2010_lci 11.00832 10.93442 11.08481
mcv_2010_lci 70.58582 73.25515 76.35556
mch_2010_lci 23.31537 24.10836 25.02775
mchc_2010_lci 32.99747 32.81518 32.72001
rbc_2010_uci 4.771034 4.619645 4.508268
hgb_2010_uci 11.28896 11.46383 11.61044
mcv_2010_uci 72.42076 76.71108 80.35341
mch_2010_uci 24.05129 25.51998 26.66990
mchc_2010_uci 33.24724 33.32706 33.25504
rbc_2010_Gsd 1.125578 1.103422 1.111690
hgb_2010_Gsd 1.117982 1.103415 1.096341
mcv_2010_Gsd 1.12041 1.10070 1.10663
mch_2010_Gsd 1.147597 1.125735 1.134474
mchc_2010_Gsd 1.033970 1.032741 1.032725
rbc_2010_min 2.92 3.41 3.62
hgb_2010_min 8.2 8.2 9.2
mcv_2010_min 50.0 58.0 56.6
mch_2010_min 15.3 17.8 17.3
mchc_2010_min 29.2 30.4 30.6
rbc_2010_max 6.14 5.66 5.84
hgb_2010_max 14.0 13.4 14.1
mcv_2010_max 97.0 87.2 92.0
mch_2010_max 35.0 30.5 30.8
mchc_2010_max 36.0 35.0 35.2
rbc_2010_IQR 0.7399998 0.6199999 0.7600000
hgb_2010_IQR 1.6 1.3 1.5
mcv_2010_IQR 11.175 10.000 7.000
mch_2010_IQR 4.600000 4.000000 2.949999
mchc_2010_IQR 1.500000 1.299999 1.399998
rbc_2010_q25 4.38 4.19 3.99
hgb_2010_q25 10.4 10.7 10.6
mcv_2010_q25 66 71 77
mch_2010_q25 21.50 23.10 24.95
mchc_2010_q25 32.4 32.5 32.4
rbc_2010_q75 5.12 4.81 4.75
hgb_2010_q75 12.0 12.0 12.1
mcv_2010_q75 77.175 81.000 84.000
mch_2010_q75 26.1 27.1 27.9
mchc_2010_q75 33.9 33.8 33.8
thal NORM HET HOM
rbc_2010_mean 4.425035 4.615792 5.113226
hgb_2010_mean 11.46170 11.27426 10.86022
mcv_2010_mean 77.70993 74.02178 65.78387
mch_2010_mean 26.14043 24.56980 21.28495
mchc_2010_mean 33.58227 33.14257 32.33333
rbc_2010_se 0.04169091 0.03442839 0.04163701
hgb_2010_se 0.10096625 0.08429247 0.10452748
mcv_2010_se 0.7005139 0.4635889 0.5227510
mch_2010_se 0.2728457 0.1830179 0.1891206
mchc_2010_se 0.08401213 0.07311559 0.08935033
rbc_2010_sd 0.4950521 0.4893194 0.4015328
hgb_2010_sd 1.198908 1.198021 1.008026
mcv_2010_sd 8.318141 6.588836 5.041228
mch_2010_sd 3.239863 2.601172 1.823813
mchc_2010_sd 0.9975887 1.0391678 0.8616634
rbc_2010_median 4.400 4.625 5.150
hgb_2010_median 11.4 11.3 10.9
mcv_2010_median 79 75 66
mch_2010_median 26.7 24.9 21.3
mchc_2010_median 33.60 33.25 32.10
rbc_2010_gmean 4.397335 4.590040 5.097141
hgb_2010_gmean 11.39739 11.20926 10.81325
mcv_2010_gmean 77.22479 73.71449 65.59098
mch_2010_gmean 25.91656 24.42079 21.20825
mchc_2010_gmean 33.56735 33.12610 32.32205
rbc_2010_lci 4.315315 4.522905 5.013292
hgb_2010_lci 11.19507 11.04144 10.60570
mcv_2010_lci 75.75692 72.77158 64.55412
mch_2010_lci 25.33610 24.04027 20.83791
mchc_2010_lci 33.39998 32.98051 32.14601
rbc_2010_uci 4.480915 4.658173 5.182392
hgb_2010_uci 11.60337 11.37962 11.02485
mcv_2010_uci 78.72111 74.66961 66.64450
mch_2010_uci 26.51031 24.80734 21.58516
mchc_2010_uci 33.73556 33.27234 32.49906
rbc_2010_Gsd 1.119728 1.112048 1.083872
hgb_2010_Gsd 1.113575 1.114853 1.098671
mcv_2010_Gsd 1.122167 1.097234 1.080443
mch_2010_Gsd 1.145737 1.119852 1.089301
mchc_2010_Gsd 1.030477 1.032259 1.026873
rbc_2010_min 2.92 3.36 4.00
hgb_2010_min 8.2 8.2 8.5
mcv_2010_min 51 54 50
mch_2010_min 15.8 16.5 15.3
mchc_2010_min 30.4 29.2 30.6
rbc_2010_max 5.86 6.14 6.04
hgb_2010_max 14.1 14.1 13.3
mcv_2010_max 97 90 82
mch_2010_max 35.0 30.0 28.1
mchc_2010_max 36.0 35.3 34.4
rbc_2010_IQR 0.6199999 0.6449997 0.5400000
hgb_2010_IQR 1.500 1.575 1.200
mcv_2010_IQR 9.000000 7.000000 5.699997
mch_2010_IQR 3.200001 2.475000 1.500000
mchc_2010_IQR 1.100002 1.200001 1.200001
rbc_2010_q25 4.1100 4.3025 4.8400
hgb_2010_q25 10.7 10.5 10.2
mcv_2010_q25 74 71 63
mch_2010_q25 25.0 23.8 20.5
mchc_2010_q25 33.1 32.6 31.7
rbc_2010_q75 4.7300 4.9475 5.3800
hgb_2010_q75 12.200 12.075 11.400
mcv_2010_q75 83.0 78.0 68.7
mch_2010_q75 28.200 26.275 22.000
mchc_2010_q75 34.2 33.8 32.9
sickle NORM HET
rbc_2010_mean 4.648995 4.720882
hgb_2010_mean 11.24212 11.27059
mcv_2010_mean 73.55707 72.91765
mch_2010_mean 24.43043 24.08824
mchc_2010_mean 33.13886 32.96765
rbc_2010_se 0.02778447 0.06624864
hgb_2010_se 0.06135666 0.14461445
mcv_2010_se 0.4275096 0.9525921
mch_2010_se 0.1684181 0.3727819
mchc_2010_se 0.05732495 0.12122150
rbc_2010_sd 0.5329986 0.5463003
hgb_2010_sd 1.177025 1.192521
mcv_2010_sd 8.201057 7.855276
mch_2010_sd 3.230820 3.074038
mchc_2010_sd 1.0996832 0.9996181
rbc_2010_median 4.66 4.75
hgb_2010_median 11.3 11.3
mcv_2010_median 75 74
mch_2010_median 24.90 24.55
mchc_2010_median 33.20 33.15
rbc_2010_gmean 4.618354 4.687932
hgb_2010_gmean 11.17926 11.20729
mcv_2010_gmean 73.08430 72.48926
mch_2010_gmean 24.20721 23.88710
mchc_2010_gmean 33.12051 32.95252
rbc_2010_lci 4.563951 4.552249
hgb_2010_lci 11.05747 10.91899
mcv_2010_lci 72.22788 70.57925
mch_2010_lci 23.86797 23.13512
mchc_2010_lci 33.00728 32.70914
rbc_2010_uci 4.673407 4.827658
hgb_2010_uci 11.30239 11.50321
mcv_2010_uci 73.95088 74.45097
mch_2010_uci 24.55128 24.66352
mchc_2010_uci 33.23412 33.19771
rbc_2010_Gsd 1.122545 1.129006
hgb_2010_Gsd 1.112779 1.113678
mcv_2010_Gsd 1.121863 1.116632
mch_2010_Gsd 1.147607 1.141278
mchc_2010_Gsd 1.033972 1.031101
rbc_2010_min 3.03 2.92
hgb_2010_min 8.2 8.2
mcv_2010_min 50 53
mch_2010_min 15.3 16.1
mchc_2010_min 29.2 30.4
rbc_2010_max 6.14 5.65
hgb_2010_max 14.1 14.0
mcv_2010_max 97 92
mch_2010_max 35 32
mchc_2010_max 36 35
rbc_2010_IQR 0.74 0.83
hgb_2010_IQR 1.5 1.5
mcv_2010_IQR 11.00 11.25
mch_2010_IQR 4.800001 4.500000
mchc_2010_IQR 1.500000 1.324999
rbc_2010_q25 4.2650 4.3225
hgb_2010_q25 10.5 10.5
mcv_2010_q25 68.00 66.75
mch_2010_q25 21.9 21.8
mchc_2010_q25 32.400 32.275
rbc_2010_q75 5.0050 5.1525
hgb_2010_q75 12 12
mcv_2010_q75 79 78
mch_2010_q75 26.7 26.3
mchc_2010_q75 33.9 33.6
g6pd_202_rtpcr NORM HET HOM/HEMI
mean 90.67375 87.25373 98.45109
se 2.314287 4.895284 4.855127
sd 40.35097 40.66328 38.53637
median 94.22998 81.22998 98.65708
min 8.722793 12.229979 15.821355
max 164.5585 168.1643 163.7885
IQR 61.32392 60.86858 51.78645
q25 61.13604 57.16427 79.77207
q75 122.4600 118.0329 131.5585
thal NORM HET HOM
mean 86.46262 92.89741 94.95957
se 3.421193 2.907411 3.791681
sd 40.62442 41.32207 36.56565
median 88.85421 94.34497 103.91992
min 8.722793 9.558522 8.722793
max 163.7885 168.1643 162.4600
q25 55.19713 61.47177 67.42710
q75 118.2300 130.0703 119.3943
sickle NORM HET
mean 90.99433 92.67405
se 2.102832 4.799763
sd 40.33931 39.57986
median 94.26283 98.68070
min 8.722793 9.459959
max 168.1643 161.8542
q25 60.68172 66.76283
q75 122.4928 127.0703
sickle NORM HET
mean 6.404185 6.887079
se 0.1694122 0.4458780
sd 3.072859 3.246041
median 6.310665 7.081610
min 0.0000000 0.1435462
max 17.50067 16.16551
q25 4.721074 5.359057
q75 8.287201 9.207834
sickle NORM HET
mean 152.320 162.826
se 3.790715 10.166472
sd 68.75733 74.01303
median 152.3442 166.6988
min 0.000000 3.398979
max 395.3092 379.9057
q25 117.7815 135.8634
q75 192.3630 211.9437

##2011 age and CBC summary (mean, sd, median) by rbc polymorphisms

##Tabulating the number of individuals with the different genotype combinations including and excluding malariapositive individuals

## <B><U>g6pd_202_rtpcr by HbS genotypes in all individuals</U></B>
## $rbc_2010
##       g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
##   NORM  279  61       58 398
##   HET    52  14       12  78
##   Sum   331  75       70 476
## 
## $hgb_2010
##       g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
##   NORM  279  61       58 398
##   HET    52  14       12  78
##   Sum   331  75       70 476
## 
## $mcv_2010
##       g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
##   NORM  279  61       58 398
##   HET    52  14       12  78
##   Sum   331  75       70 476
## 
## $mch_2010
##       g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
##   NORM  279  61       58 398
##   HET    52  14       12  78
##   Sum   331  75       70 476
## 
## $mchc_2010
##       g6pd_202_rtpcr
## sickle NORM HET HOM/HEMI Sum
##   NORM  279  61       58 398
##   HET    52  14       12  78
##   Sum   331  75       70 476
## <B><U>g6pd_202_rtpcr by thal genotypes in all individuals</U></B>
## $rbc_2010
##       g6pd_202_rtpcr
## thal   NORM HET HOM/HEMI Sum
##   NORM  111  23       22 156
##   HET   143  40       38 221
##   HOM    77  12       10  99
##   Sum   331  75       70 476
## 
## $hgb_2010
##       g6pd_202_rtpcr
## thal   NORM HET HOM/HEMI Sum
##   NORM  111  23       22 156
##   HET   143  40       38 221
##   HOM    77  12       10  99
##   Sum   331  75       70 476
## 
## $mcv_2010
##       g6pd_202_rtpcr
## thal   NORM HET HOM/HEMI Sum
##   NORM  111  23       22 156
##   HET   143  40       38 221
##   HOM    77  12       10  99
##   Sum   331  75       70 476
## 
## $mch_2010
##       g6pd_202_rtpcr
## thal   NORM HET HOM/HEMI Sum
##   NORM  111  23       22 156
##   HET   143  40       38 221
##   HOM    77  12       10  99
##   Sum   331  75       70 476
## 
## $mchc_2010
##       g6pd_202_rtpcr
## thal   NORM HET HOM/HEMI Sum
##   NORM  111  23       22 156
##   HET   143  40       38 221
##   HOM    77  12       10  99
##   Sum   331  75       70 476
## <B><U>g6pd_202_rtpcr by thal genotypes in all individuals</U></B>
## 
## ----------------------------------------
##   &nbsp;    NORM   HET   HOM/HEMI   Sum 
## ---------- ------ ----- ---------- -----
##  **NORM**   111    23       22      156 
## 
##  **HET**    143    40       38      221 
## 
##  **HOM**     77    12       10      99  
## 
##  **Sum**    331    75       70      476 
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by HBS genotypes in all individuals</U></B>
## 
## ----------------------------------------
##   &nbsp;    NORM   HET   HOM/HEMI   Sum 
## ---------- ------ ----- ---------- -----
##  **NORM**   279    61       58      398 
## 
##  **HET**     52    14       12      78  
## 
##  **Sum**    331    75       70      476 
## ----------------------------------------
## <B><U>thal by HbS genotypes in all individuals</U></B>
## 
## -----------------------------------
##   &nbsp;    NORM   HET   HOM   Sum 
## ---------- ------ ----- ----- -----
##  **NORM**   130    186   82    398 
## 
##  **HET**     26    35    17    78  
## 
##  **Sum**    156    221   99    476 
## -----------------------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in all individuals</U></B>
## 
## --------------------------------------
##  g6pd_202_rtpcr   thal   sickle    n  
## ---------------- ------ -------- -----
##       NORM        NORM    NORM    94  
## 
##       NORM        NORM    HET     17  
## 
##       NORM        HET     NORM    122 
## 
##       NORM        HET     HET     21  
## 
##       NORM        HOM     NORM    63  
## 
##       NORM        HOM     HET     14  
## 
##       HET         NORM    NORM    17  
## 
##       HET         NORM    HET      6  
## 
##       HET         HET     NORM    32  
## 
##       HET         HET     HET      8  
## 
##       HET         HOM     NORM    12  
## 
##     HOM/HEMI      NORM    NORM    19  
## 
##     HOM/HEMI      NORM    HET      3  
## 
##     HOM/HEMI      HET     NORM    32  
## 
##     HOM/HEMI      HET     HET      6  
## 
##     HOM/HEMI      HOM     NORM     7  
## 
##     HOM/HEMI      HOM     HET      3  
## --------------------------------------
## 
## ----------------------------------------------
##  g6pd_202_rtpcr   thal   sickle    sex     n  
## ---------------- ------ -------- -------- ----
##       NORM        NORM    NORM    FEMALE   36 
## 
##       NORM        NORM    NORM     MALE    58 
## 
##       NORM        NORM    HET     FEMALE   8  
## 
##       NORM        NORM    HET      MALE    9  
## 
##       NORM        HET     NORM    FEMALE   57 
## 
##       NORM        HET     NORM     MALE    65 
## 
##       NORM        HET     HET     FEMALE   11 
## 
##       NORM        HET     HET      MALE    10 
## 
##       NORM        HOM     NORM    FEMALE   29 
## 
##       NORM        HOM     NORM     MALE    34 
## 
##       NORM        HOM     HET     FEMALE   5  
## 
##       NORM        HOM     HET      MALE    9  
## 
##       HET         NORM    NORM    FEMALE   17 
## 
##       HET         NORM    HET     FEMALE   6  
## 
##       HET         HET     NORM    FEMALE   32 
## 
##       HET         HET     HET     FEMALE   8  
## 
##       HET         HOM     NORM    FEMALE   12 
## 
##     HOM/HEMI      NORM    NORM    FEMALE   4  
## 
##     HOM/HEMI      NORM    NORM     MALE    15 
## 
##     HOM/HEMI      NORM    HET     FEMALE   1  
## 
##     HOM/HEMI      NORM    HET      MALE    2  
## 
##     HOM/HEMI      HET     NORM    FEMALE   4  
## 
##     HOM/HEMI      HET     NORM     MALE    28 
## 
##     HOM/HEMI      HET     HET      MALE    6  
## 
##     HOM/HEMI      HOM     NORM    FEMALE   2  
## 
##     HOM/HEMI      HOM     NORM     MALE    5  
## 
##     HOM/HEMI      HOM     HET     FEMALE   1  
## 
##     HOM/HEMI      HOM     HET      MALE    2  
## ----------------------------------------------
## 
## -------------------------------------------------------------
##  g6pd_202_rtpcr   thal   sickle      malaria_status       n  
## ---------------- ------ -------- ----------------------- ----
##       NORM        NORM    NORM         no_malaria         75 
## 
##       NORM        NORM    NORM    assymptomatic_malaria   9  
## 
##       NORM        NORM    NORM    uncomplicated_malaria   10 
## 
##       NORM        NORM    HET          no_malaria         13 
## 
##       NORM        NORM    HET     assymptomatic_malaria   3  
## 
##       NORM        NORM    HET     uncomplicated_malaria   1  
## 
##       NORM        HET     NORM         no_malaria         99 
## 
##       NORM        HET     NORM    assymptomatic_malaria   14 
## 
##       NORM        HET     NORM    uncomplicated_malaria   9  
## 
##       NORM        HET     HET          no_malaria         17 
## 
##       NORM        HET     HET     assymptomatic_malaria   1  
## 
##       NORM        HET     HET     uncomplicated_malaria   3  
## 
##       NORM        HOM     NORM         no_malaria         48 
## 
##       NORM        HOM     NORM    assymptomatic_malaria   11 
## 
##       NORM        HOM     NORM    uncomplicated_malaria   4  
## 
##       NORM        HOM     HET          no_malaria         12 
## 
##       NORM        HOM     HET     assymptomatic_malaria   2  
## 
##       HET         NORM    NORM         no_malaria         14 
## 
##       HET         NORM    NORM    assymptomatic_malaria   2  
## 
##       HET         NORM    NORM    uncomplicated_malaria   1  
## 
##       HET         NORM    HET          no_malaria         4  
## 
##       HET         NORM    HET     assymptomatic_malaria   1  
## 
##       HET         NORM    HET     uncomplicated_malaria   1  
## 
##       HET         HET     NORM         no_malaria         24 
## 
##       HET         HET     NORM    assymptomatic_malaria   7  
## 
##       HET         HET     NORM    uncomplicated_malaria   1  
## 
##       HET         HET     HET          no_malaria         5  
## 
##       HET         HET     HET     assymptomatic_malaria   1  
## 
##       HET         HET     HET     uncomplicated_malaria   2  
## 
##       HET         HOM     NORM         no_malaria         11 
## 
##       HET         HOM     NORM    uncomplicated_malaria   1  
## 
##     HOM/HEMI      NORM    NORM         no_malaria         16 
## 
##     HOM/HEMI      NORM    NORM    assymptomatic_malaria   1  
## 
##     HOM/HEMI      NORM    NORM    uncomplicated_malaria   2  
## 
##     HOM/HEMI      NORM    HET          no_malaria         3  
## 
##     HOM/HEMI      HET     NORM         no_malaria         25 
## 
##     HOM/HEMI      HET     NORM    assymptomatic_malaria   5  
## 
##     HOM/HEMI      HET     NORM    uncomplicated_malaria   2  
## 
##     HOM/HEMI      HET     HET          no_malaria         4  
## 
##     HOM/HEMI      HET     HET     uncomplicated_malaria   2  
## 
##     HOM/HEMI      HOM     NORM         no_malaria         6  
## 
##     HOM/HEMI      HOM     NORM    assymptomatic_malaria   1  
## 
##     HOM/HEMI      HOM     HET          no_malaria         2  
## 
##     HOM/HEMI      HOM     HET     uncomplicated_malaria   1  
## -------------------------------------------------------------
## 
## --------------------------------------
##  g6pd_202_rtpcr   thal   sickle    n  
## ---------------- ------ -------- -----
##       NORM        NORM    NORM    85  
## 
##       NORM        NORM    HET     15  
## 
##       NORM        HET     NORM    118 
## 
##       NORM        HET     HET     15  
## 
##       NORM        HOM     NORM    57  
## 
##       NORM        HOM     HET     12  
## 
##       HET         NORM    NORM    17  
## 
##       HET         NORM    HET      6  
## 
##       HET         HET     NORM    31  
## 
##       HET         HET     HET      5  
## 
##       HET         HOM     NORM    10  
## 
##     HOM/HEMI      NORM    NORM    11  
## 
##     HOM/HEMI      NORM    HET      1  
## 
##     HOM/HEMI      HET     NORM    18  
## 
##     HOM/HEMI      HET     HET      4  
## 
##     HOM/HEMI      HOM     NORM     7  
## 
##     HOM/HEMI      HOM     HET      1  
## --------------------------------------
## 
## -----------------------------
##  g6pd_202_rtpcr   thal    n  
## ---------------- ------ -----
##       NORM        NORM   111 
## 
##       NORM        HET    143 
## 
##       NORM        HOM    77  
## 
##       HET         NORM   23  
## 
##       HET         HET    40  
## 
##       HET         HOM    12  
## 
##     HOM/HEMI      NORM   22  
## 
##     HOM/HEMI      HET    38  
## 
##     HOM/HEMI      HOM    10  
## -----------------------------
## 
## -------------------------------------
##  g6pd_202_rtpcr   thal    sex     n  
## ---------------- ------ -------- ----
##       NORM        NORM   FEMALE   44 
## 
##       NORM        NORM    MALE    67 
## 
##       NORM        HET    FEMALE   68 
## 
##       NORM        HET     MALE    75 
## 
##       NORM        HOM    FEMALE   34 
## 
##       NORM        HOM     MALE    43 
## 
##       HET         NORM   FEMALE   23 
## 
##       HET         HET    FEMALE   40 
## 
##       HET         HOM    FEMALE   12 
## 
##     HOM/HEMI      NORM   FEMALE   5  
## 
##     HOM/HEMI      NORM    MALE    17 
## 
##     HOM/HEMI      HET    FEMALE   4  
## 
##     HOM/HEMI      HET     MALE    34 
## 
##     HOM/HEMI      HOM    FEMALE   3  
## 
##     HOM/HEMI      HOM     MALE    7  
## -------------------------------------
## 
## -----------------------------------------------------
##  g6pd_202_rtpcr   thal      malaria_status        n  
## ---------------- ------ ----------------------- -----
##       NORM        NORM        no_malaria         88  
## 
##       NORM        NORM   assymptomatic_malaria   12  
## 
##       NORM        NORM   uncomplicated_malaria   11  
## 
##       NORM        HET         no_malaria         116 
## 
##       NORM        HET    assymptomatic_malaria   15  
## 
##       NORM        HET    uncomplicated_malaria   12  
## 
##       NORM        HOM         no_malaria         60  
## 
##       NORM        HOM    assymptomatic_malaria   13  
## 
##       NORM        HOM    uncomplicated_malaria    4  
## 
##       HET         NORM        no_malaria         18  
## 
##       HET         NORM   assymptomatic_malaria    3  
## 
##       HET         NORM   uncomplicated_malaria    2  
## 
##       HET         HET         no_malaria         29  
## 
##       HET         HET    assymptomatic_malaria    8  
## 
##       HET         HET    uncomplicated_malaria    3  
## 
##       HET         HOM         no_malaria         11  
## 
##       HET         HOM    uncomplicated_malaria    1  
## 
##     HOM/HEMI      NORM        no_malaria         19  
## 
##     HOM/HEMI      NORM   assymptomatic_malaria    1  
## 
##     HOM/HEMI      NORM   uncomplicated_malaria    2  
## 
##     HOM/HEMI      HET         no_malaria         29  
## 
##     HOM/HEMI      HET    assymptomatic_malaria    5  
## 
##     HOM/HEMI      HET    uncomplicated_malaria    4  
## 
##     HOM/HEMI      HOM         no_malaria          8  
## 
##     HOM/HEMI      HOM    assymptomatic_malaria    1  
## 
##     HOM/HEMI      HOM    uncomplicated_malaria    1  
## -----------------------------------------------------
## 
## -----------------------------
##  g6pd_202_rtpcr   thal    n  
## ---------------- ------ -----
##       NORM        NORM   100 
## 
##       NORM        HET    133 
## 
##       NORM        HOM    69  
## 
##       HET         NORM   23  
## 
##       HET         HET    36  
## 
##       HET         HOM    10  
## 
##     HOM/HEMI      NORM   12  
## 
##     HOM/HEMI      HET    22  
## 
##     HOM/HEMI      HOM     8  
## -----------------------------
## 
## -------------------------------
##  g6pd_202_rtpcr   sickle    n  
## ---------------- -------- -----
##       NORM         NORM    279 
## 
##       NORM         HET     52  
## 
##       HET          NORM    61  
## 
##       HET          HET     14  
## 
##     HOM/HEMI       NORM    58  
## 
##     HOM/HEMI       HET     12  
## -------------------------------
## 
## -------------------------------------------------------
##  g6pd_202_rtpcr   sickle      malaria_status        n  
## ---------------- -------- ----------------------- -----
##       NORM         NORM         no_malaria         222 
## 
##       NORM         NORM    assymptomatic_malaria   34  
## 
##       NORM         NORM    uncomplicated_malaria   23  
## 
##       NORM         HET          no_malaria         42  
## 
##       NORM         HET     assymptomatic_malaria    6  
## 
##       NORM         HET     uncomplicated_malaria    4  
## 
##       HET          NORM         no_malaria         49  
## 
##       HET          NORM    assymptomatic_malaria    9  
## 
##       HET          NORM    uncomplicated_malaria    3  
## 
##       HET          HET          no_malaria          9  
## 
##       HET          HET     assymptomatic_malaria    2  
## 
##       HET          HET     uncomplicated_malaria    3  
## 
##     HOM/HEMI       NORM         no_malaria         47  
## 
##     HOM/HEMI       NORM    assymptomatic_malaria    7  
## 
##     HOM/HEMI       NORM    uncomplicated_malaria    4  
## 
##     HOM/HEMI       HET          no_malaria          9  
## 
##     HOM/HEMI       HET     uncomplicated_malaria    3  
## -------------------------------------------------------
## 
## ----------------------------------------
##  g6pd_202_rtpcr   sickle    sex      n  
## ---------------- -------- -------- -----
##       NORM         NORM    FEMALE   122 
## 
##       NORM         NORM     MALE    157 
## 
##       NORM         HET     FEMALE   24  
## 
##       NORM         HET      MALE    28  
## 
##       HET          NORM    FEMALE   61  
## 
##       HET          HET     FEMALE   14  
## 
##     HOM/HEMI       NORM    FEMALE   10  
## 
##     HOM/HEMI       NORM     MALE    48  
## 
##     HOM/HEMI       HET     FEMALE    2  
## 
##     HOM/HEMI       HET      MALE    10  
## ----------------------------------------
## 
## -------------------------------
##  g6pd_202_rtpcr   sickle    n  
## ---------------- -------- -----
##       NORM         NORM    260 
## 
##       NORM         HET     42  
## 
##       HET          NORM    58  
## 
##       HET          HET     11  
## 
##     HOM/HEMI       NORM    36  
## 
##     HOM/HEMI       HET      6  
## -------------------------------
## <B><U>g6pd_202_rtpcr, thal and HbS genotypes by sex and malaria status</U></B>
## 
## ----------------------------------------------
##  g6pd_202_rtpcr      malaria_status        n  
## ---------------- ----------------------- -----
##       NORM             no_malaria         264 
## 
##       NORM        assymptomatic_malaria   40  
## 
##       NORM        uncomplicated_malaria   27  
## 
##       HET              no_malaria         58  
## 
##       HET         assymptomatic_malaria   11  
## 
##       HET         uncomplicated_malaria    6  
## 
##     HOM/HEMI           no_malaria         56  
## 
##     HOM/HEMI      assymptomatic_malaria    7  
## 
##     HOM/HEMI      uncomplicated_malaria    7  
## ----------------------------------------------
## 
## -------------------------------
##  g6pd_202_rtpcr    sex      n  
## ---------------- -------- -----
##       NORM        FEMALE   146 
## 
##       NORM         MALE    185 
## 
##       HET         FEMALE   75  
## 
##     HOM/HEMI      FEMALE   12  
## 
##     HOM/HEMI       MALE    58  
## -------------------------------
## 
## --------------------------------------
##  sickle      malaria_status        n  
## -------- ----------------------- -----
##   NORM         no_malaria         318 
## 
##   NORM    assymptomatic_malaria   50  
## 
##   NORM    uncomplicated_malaria   30  
## 
##   HET          no_malaria         60  
## 
##   HET     assymptomatic_malaria    8  
## 
##   HET     uncomplicated_malaria   10  
## --------------------------------------
## 
## -----------------------
##  sickle    sex      n  
## -------- -------- -----
##   NORM    FEMALE   193 
## 
##   NORM     MALE    205 
## 
##   HET     FEMALE   40  
## 
##   HET      MALE    38  
## -----------------------
## 
## ------------------------------------
##  thal      malaria_status        n  
## ------ ----------------------- -----
##  NORM        no_malaria         125 
## 
##  NORM   assymptomatic_malaria   16  
## 
##  NORM   uncomplicated_malaria   15  
## 
##  HET         no_malaria         174 
## 
##  HET    assymptomatic_malaria   28  
## 
##  HET    uncomplicated_malaria   19  
## 
##  HOM         no_malaria         79  
## 
##  HOM    assymptomatic_malaria   14  
## 
##  HOM    uncomplicated_malaria    6  
## ------------------------------------
## 
## ---------------------
##  thal    sex      n  
## ------ -------- -----
##  NORM   FEMALE   72  
## 
##  NORM    MALE    84  
## 
##  HET    FEMALE   112 
## 
##  HET     MALE    109 
## 
##  HOM    FEMALE   49  
## 
##  HOM     MALE    50  
## ---------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in individuals with g6pd activity</U></B>
## 
## --------------------------------------
##  g6pd_202_rtpcr   thal   sickle    n  
## ---------------- ------ -------- -----
##       NORM        NORM    NORM    85  
## 
##       NORM        NORM    HET     15  
## 
##       NORM        HET     NORM    118 
## 
##       NORM        HET     HET     15  
## 
##       NORM        HOM     NORM    57  
## 
##       NORM        HOM     HET     12  
## 
##       HET         NORM    NORM    17  
## 
##       HET         NORM    HET      6  
## 
##       HET         HET     NORM    31  
## 
##       HET         HET     HET      5  
## 
##       HET         HOM     NORM    10  
## 
##     HOM/HEMI      NORM    NORM    11  
## 
##     HOM/HEMI      NORM    HET      1  
## 
##     HOM/HEMI      HET     NORM    18  
## 
##     HOM/HEMI      HET     HET      4  
## 
##     HOM/HEMI      HOM     NORM     7  
## 
##     HOM/HEMI      HOM     HET      1  
## --------------------------------------
## 
## ----------------------------------------------
##  g6pd_202_rtpcr   thal   sickle    sex     n  
## ---------------- ------ -------- -------- ----
##       NORM        NORM    NORM    FEMALE   31 
## 
##       NORM        NORM    NORM     MALE    54 
## 
##       NORM        NORM    HET     FEMALE   7  
## 
##       NORM        NORM    HET      MALE    8  
## 
##       NORM        HET     NORM    FEMALE   55 
## 
##       NORM        HET     NORM     MALE    63 
## 
##       NORM        HET     HET     FEMALE   9  
## 
##       NORM        HET     HET      MALE    6  
## 
##       NORM        HOM     NORM    FEMALE   27 
## 
##       NORM        HOM     NORM     MALE    30 
## 
##       NORM        HOM     HET     FEMALE   4  
## 
##       NORM        HOM     HET      MALE    8  
## 
##       HET         NORM    NORM    FEMALE   17 
## 
##       HET         NORM    HET     FEMALE   6  
## 
##       HET         HET     NORM    FEMALE   31 
## 
##       HET         HET     HET     FEMALE   5  
## 
##       HET         HOM     NORM    FEMALE   10 
## 
##     HOM/HEMI      NORM    NORM    FEMALE   2  
## 
##     HOM/HEMI      NORM    NORM     MALE    9  
## 
##     HOM/HEMI      NORM    HET      MALE    1  
## 
##     HOM/HEMI      HET     NORM    FEMALE   2  
## 
##     HOM/HEMI      HET     NORM     MALE    16 
## 
##     HOM/HEMI      HET     HET      MALE    4  
## 
##     HOM/HEMI      HOM     NORM    FEMALE   2  
## 
##     HOM/HEMI      HOM     NORM     MALE    5  
## 
##     HOM/HEMI      HOM     HET     FEMALE   1  
## ----------------------------------------------
## 
## -------------------------------------------------------------
##  g6pd_202_rtpcr   thal   sickle      malaria_status       n  
## ---------------- ------ -------- ----------------------- ----
##       NORM        NORM    NORM         no_malaria         70 
## 
##       NORM        NORM    NORM    assymptomatic_malaria   8  
## 
##       NORM        NORM    NORM    uncomplicated_malaria   7  
## 
##       NORM        NORM    HET          no_malaria         12 
## 
##       NORM        NORM    HET     assymptomatic_malaria   3  
## 
##       NORM        HET     NORM         no_malaria         96 
## 
##       NORM        HET     NORM    assymptomatic_malaria   13 
## 
##       NORM        HET     NORM    uncomplicated_malaria   9  
## 
##       NORM        HET     HET          no_malaria         12 
## 
##       NORM        HET     HET     assymptomatic_malaria   1  
## 
##       NORM        HET     HET     uncomplicated_malaria   2  
## 
##       NORM        HOM     NORM         no_malaria         43 
## 
##       NORM        HOM     NORM    assymptomatic_malaria   10 
## 
##       NORM        HOM     NORM    uncomplicated_malaria   4  
## 
##       NORM        HOM     HET          no_malaria         10 
## 
##       NORM        HOM     HET     assymptomatic_malaria   2  
## 
##       HET         NORM    NORM         no_malaria         14 
## 
##       HET         NORM    NORM    assymptomatic_malaria   2  
## 
##       HET         NORM    NORM    uncomplicated_malaria   1  
## 
##       HET         NORM    HET          no_malaria         4  
## 
##       HET         NORM    HET     assymptomatic_malaria   1  
## 
##       HET         NORM    HET     uncomplicated_malaria   1  
## 
##       HET         HET     NORM         no_malaria         23 
## 
##       HET         HET     NORM    assymptomatic_malaria   7  
## 
##       HET         HET     NORM    uncomplicated_malaria   1  
## 
##       HET         HET     HET          no_malaria         3  
## 
##       HET         HET     HET     assymptomatic_malaria   1  
## 
##       HET         HET     HET     uncomplicated_malaria   1  
## 
##       HET         HOM     NORM         no_malaria         9  
## 
##       HET         HOM     NORM    uncomplicated_malaria   1  
## 
##     HOM/HEMI      NORM    NORM         no_malaria         11 
## 
##     HOM/HEMI      NORM    HET          no_malaria         1  
## 
##     HOM/HEMI      HET     NORM         no_malaria         12 
## 
##     HOM/HEMI      HET     NORM    assymptomatic_malaria   4  
## 
##     HOM/HEMI      HET     NORM    uncomplicated_malaria   2  
## 
##     HOM/HEMI      HET     HET          no_malaria         3  
## 
##     HOM/HEMI      HET     HET     uncomplicated_malaria   1  
## 
##     HOM/HEMI      HOM     NORM         no_malaria         6  
## 
##     HOM/HEMI      HOM     NORM    assymptomatic_malaria   1  
## 
##     HOM/HEMI      HOM     HET     uncomplicated_malaria   1  
## -------------------------------------------------------------
## 
## -----------------------------
##  g6pd_202_rtpcr   thal    n  
## ---------------- ------ -----
##       NORM        NORM   100 
## 
##       NORM        HET    133 
## 
##       NORM        HOM    69  
## 
##       HET         NORM   23  
## 
##       HET         HET    36  
## 
##       HET         HOM    10  
## 
##     HOM/HEMI      NORM   12  
## 
##     HOM/HEMI      HET    22  
## 
##     HOM/HEMI      HOM     8  
## -----------------------------
## 
## -------------------------------------
##  g6pd_202_rtpcr   thal    sex     n  
## ---------------- ------ -------- ----
##       NORM        NORM   FEMALE   38 
## 
##       NORM        NORM    MALE    62 
## 
##       NORM        HET    FEMALE   64 
## 
##       NORM        HET     MALE    69 
## 
##       NORM        HOM    FEMALE   31 
## 
##       NORM        HOM     MALE    38 
## 
##       HET         NORM   FEMALE   23 
## 
##       HET         HET    FEMALE   36 
## 
##       HET         HOM    FEMALE   10 
## 
##     HOM/HEMI      NORM   FEMALE   2  
## 
##     HOM/HEMI      NORM    MALE    10 
## 
##     HOM/HEMI      HET    FEMALE   2  
## 
##     HOM/HEMI      HET     MALE    20 
## 
##     HOM/HEMI      HOM    FEMALE   3  
## 
##     HOM/HEMI      HOM     MALE    5  
## -------------------------------------
## 
## -----------------------------------------------------
##  g6pd_202_rtpcr   thal      malaria_status        n  
## ---------------- ------ ----------------------- -----
##       NORM        NORM        no_malaria         82  
## 
##       NORM        NORM   assymptomatic_malaria   11  
## 
##       NORM        NORM   uncomplicated_malaria    7  
## 
##       NORM        HET         no_malaria         108 
## 
##       NORM        HET    assymptomatic_malaria   14  
## 
##       NORM        HET    uncomplicated_malaria   11  
## 
##       NORM        HOM         no_malaria         53  
## 
##       NORM        HOM    assymptomatic_malaria   12  
## 
##       NORM        HOM    uncomplicated_malaria    4  
## 
##       HET         NORM        no_malaria         18  
## 
##       HET         NORM   assymptomatic_malaria    3  
## 
##       HET         NORM   uncomplicated_malaria    2  
## 
##       HET         HET         no_malaria         26  
## 
##       HET         HET    assymptomatic_malaria    8  
## 
##       HET         HET    uncomplicated_malaria    2  
## 
##       HET         HOM         no_malaria          9  
## 
##       HET         HOM    uncomplicated_malaria    1  
## 
##     HOM/HEMI      NORM        no_malaria         12  
## 
##     HOM/HEMI      HET         no_malaria         15  
## 
##     HOM/HEMI      HET    assymptomatic_malaria    4  
## 
##     HOM/HEMI      HET    uncomplicated_malaria    3  
## 
##     HOM/HEMI      HOM         no_malaria          6  
## 
##     HOM/HEMI      HOM    assymptomatic_malaria    1  
## 
##     HOM/HEMI      HOM    uncomplicated_malaria    1  
## -----------------------------------------------------
## 
## -------------------------------
##  g6pd_202_rtpcr   sickle    n  
## ---------------- -------- -----
##       NORM         NORM    260 
## 
##       NORM         HET     42  
## 
##       HET          NORM    58  
## 
##       HET          HET     11  
## 
##     HOM/HEMI       NORM    36  
## 
##     HOM/HEMI       HET      6  
## -------------------------------
## 
## -------------------------------------------------------
##  g6pd_202_rtpcr   sickle      malaria_status        n  
## ---------------- -------- ----------------------- -----
##       NORM         NORM         no_malaria         209 
## 
##       NORM         NORM    assymptomatic_malaria   31  
## 
##       NORM         NORM    uncomplicated_malaria   20  
## 
##       NORM         HET          no_malaria         34  
## 
##       NORM         HET     assymptomatic_malaria    6  
## 
##       NORM         HET     uncomplicated_malaria    2  
## 
##       HET          NORM         no_malaria         46  
## 
##       HET          NORM    assymptomatic_malaria    9  
## 
##       HET          NORM    uncomplicated_malaria    3  
## 
##       HET          HET          no_malaria          7  
## 
##       HET          HET     assymptomatic_malaria    2  
## 
##       HET          HET     uncomplicated_malaria    2  
## 
##     HOM/HEMI       NORM         no_malaria         29  
## 
##     HOM/HEMI       NORM    assymptomatic_malaria    5  
## 
##     HOM/HEMI       NORM    uncomplicated_malaria    2  
## 
##     HOM/HEMI       HET          no_malaria          4  
## 
##     HOM/HEMI       HET     uncomplicated_malaria    2  
## -------------------------------------------------------
## 
## ----------------------------------------
##  g6pd_202_rtpcr   sickle    sex      n  
## ---------------- -------- -------- -----
##       NORM         NORM    FEMALE   113 
## 
##       NORM         NORM     MALE    147 
## 
##       NORM         HET     FEMALE   20  
## 
##       NORM         HET      MALE    22  
## 
##       HET          NORM    FEMALE   58  
## 
##       HET          HET     FEMALE   11  
## 
##     HOM/HEMI       NORM    FEMALE    6  
## 
##     HOM/HEMI       NORM     MALE    30  
## 
##     HOM/HEMI       HET     FEMALE    1  
## 
##     HOM/HEMI       HET      MALE     5  
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by HbS genotypes in malaria negative individuals</U></B>
## 
## ----------------------------------------
##   &nbsp;    NORM   HET   HOM/HEMI   Sum 
## ---------- ------ ----- ---------- -----
##  **NORM**   256    58       54      368 
## 
##  **HET**     48    11       9       68  
## 
##  **Sum**    304    69       63      436 
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by thal genotypes in malaria negative individuals</U></B>
## 
## ----------------------------------------
##   &nbsp;    NORM   HET   HOM/HEMI   Sum 
## ---------- ------ ----- ---------- -----
##  **NORM**   100    21       20      141 
## 
##  **HET**    131    37       34      202 
## 
##  **HOM**     73    11       9       93  
## 
##  **Sum**    304    69       63      436 
## ----------------------------------------
## <B><U>thal by HbS genotypes in malaria negative individuals</U></B>
## 
## -----------------------------------
##   &nbsp;    NORM   HET   HOM   Sum 
## ---------- ------ ----- ----- -----
##  **NORM**   117    174   77    368 
## 
##  **HET**     24    28    16    68  
## 
##  **Sum**    141    202   93    436 
## -----------------------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in malaria negative individuals</U></B>
## 
## --------------------------------------
##  g6pd_202_rtpcr   thal   sickle    n  
## ---------------- ------ -------- -----
##       NORM        NORM    NORM    84  
## 
##       NORM        NORM    HET     16  
## 
##       NORM        HET     NORM    113 
## 
##       NORM        HET     HET     18  
## 
##       NORM        HOM     NORM    59  
## 
##       NORM        HOM     HET     14  
## 
##       HET         NORM    NORM    16  
## 
##       HET         NORM    HET      5  
## 
##       HET         HET     NORM    31  
## 
##       HET         HET     HET      6  
## 
##       HET         HOM     NORM    11  
## 
##     HOM/HEMI      NORM    NORM    17  
## 
##     HOM/HEMI      NORM    HET      3  
## 
##     HOM/HEMI      HET     NORM    30  
## 
##     HOM/HEMI      HET     HET      4  
## 
##     HOM/HEMI      HOM     NORM     7  
## 
##     HOM/HEMI      HOM     HET      2  
## --------------------------------------
## <B><U>g6pd_202_rtpcr by thal genotypes in all individuals with enzyme activity</U></B>
## 
## ----------------------------------------
##   &nbsp;    NORM   HET   HOM/HEMI   Sum 
## ---------- ------ ----- ---------- -----
##  **NORM**   100    23       12      135 
## 
##  **HET**    133    36       22      191 
## 
##  **HOM**     69    10       8       87  
## 
##  **Sum**    302    69       42      413 
## ----------------------------------------
## <B><U>g6pd_202_rtpcr by HBS genotypes in all individuals with enzyme activity</U></B>
## 
## ----------------------------------------
##   &nbsp;    NORM   HET   HOM/HEMI   Sum 
## ---------- ------ ----- ---------- -----
##  **NORM**   260    58       36      354 
## 
##  **HET**     42    11       6       59  
## 
##  **Sum**    302    69       42      413 
## ----------------------------------------
## <B><U>thal by HbS genotypes in all individuals with enzyme activity</U></B>
## 
## -----------------------------------
##   &nbsp;    NORM   HET   HOM   Sum 
## ---------- ------ ----- ----- -----
##  **NORM**   113    167   74    354 
## 
##  **HET**     22    24    13    59  
## 
##  **Sum**    135    191   87    413 
## -----------------------------------
## <B><U>combinations of g6pd_202_rtpcr, thal and HbS genotypes in all individuals with enzyme activity</U></B>
## 
## --------------------------------------
##  g6pd_202_rtpcr   thal   sickle    n  
## ---------------- ------ -------- -----
##       NORM        NORM    NORM    85  
## 
##       NORM        NORM    HET     15  
## 
##       NORM        HET     NORM    118 
## 
##       NORM        HET     HET     15  
## 
##       NORM        HOM     NORM    57  
## 
##       NORM        HOM     HET     12  
## 
##       HET         NORM    NORM    17  
## 
##       HET         NORM    HET      6  
## 
##       HET         HET     NORM    31  
## 
##       HET         HET     HET      5  
## 
##       HET         HOM     NORM    10  
## 
##     HOM/HEMI      NORM    NORM    11  
## 
##     HOM/HEMI      NORM    HET      1  
## 
##     HOM/HEMI      HET     NORM    18  
## 
##     HOM/HEMI      HET     HET      4  
## 
##     HOM/HEMI      HOM     NORM     7  
## 
##     HOM/HEMI      HOM     HET      1  
## --------------------------------------
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    8.60    8.85    9.10    9.10    9.35    9.60      29
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    8.20   10.50   11.30   11.24   12.00   14.10      29
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "hgb_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_ghb3"] > 15
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 6.929071
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.008480574
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## 0.01640 0.01735 0.01830 0.01853 0.01960 0.02090      29
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## 0.00000 0.00810 0.01000 0.01009 0.01205 0.02300      29
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_ghb3"] > 15
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 8.453745
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.003642925
## 
## FALSE  TRUE 
##   299     3
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    11.3    11.3    11.3    11.3    11.3    11.3       6
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     8.2    10.5    11.4    11.2    12.0    13.4       6
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "hgb_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_ghb3"] > 11
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 0.09105572
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.7628393
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.018   0.018   0.018   0.018   0.018   0.018       6
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.000500 0.005075 0.007000 0.007074 0.008750 0.014000        6
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_ghb3"] > 11
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 2.916204
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.08769396
## 
## FALSE  TRUE 
##    68     1
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.80   10.90   11.20   11.57   12.00   14.10      28
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.20   10.40   11.30   11.26   12.10   13.10      28
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "hgb_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_ghb3"] > 2.5
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 0.1716228
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.6786735
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.004500 0.007700 0.008000 0.008867 0.011300 0.011900       28
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.000000 0.000400 0.000800 0.000888 0.001300 0.003000       28
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_ghb3"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_ghb3"] > 2.5
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 20.77818
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         5.156729e-06
## 
## FALSE  TRUE 
##    33     9
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   2.920   3.393   3.900   3.930   4.438   5.000      29
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.030   4.380   4.740   4.735   5.100   6.140      29
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "rbc_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_rcc"] > 350
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 3.585663
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.05828017
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## 0.01380 0.01650 0.01915 0.01878 0.02142 0.02300      29
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## 0.00000 0.00810 0.01000 0.01006 0.01197 0.01980      29
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "u_rcc"] > 350
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 10.46008
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.001219821
## 
## FALSE  TRUE 
##   298     4
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.47    4.47    4.47    4.47    4.47    4.47       6
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.410   4.170   4.405   4.496   4.780   5.490       6
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "rbc_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_rcc"] > 300
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 0.002521607
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.9599506
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.018   0.018   0.018   0.018   0.018   0.018       6
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.000500 0.005075 0.007000 0.007074 0.008750 0.014000        6
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "u_rcc"] > 300
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 2.916204
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.08769396
## 
## FALSE  TRUE 
##    68     1
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.620   4.125   4.300   4.335   4.487   5.170      28
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.760   4.058   4.540   4.469   4.782   5.840      28
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "rbc_2010"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_rcc"] > 100
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 0.3705932
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         0.5426811
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.006800 0.007775 0.009200 0.009413 0.011325 0.011900       28
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## 0.000000 0.000425 0.000800 0.000994 0.001450 0.004500       28
## 
## Results of Hypothesis Test
## --------------------------
## 
## Alternative Hypothesis:          
## 
## Test Name:                       Kruskal-Wallis rank sum test
## 
## Data:                            pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "g6p_odmin"] by pgd_genopheno_01042018[!is.na(pgd_genopheno_01042018[, "u_rcc"]) & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HOM/HEMI", "u_rcc"] > 100
## 
## Test Statistic:                  Kruskal-Wallis chi-squared = 19.02917
## 
## Test Statistic Parameter:        df = 1
## 
## P-value:                         1.287353e-05
## 
## FALSE  TRUE 
##    34     8

#ASSOCIATION TESTS

##2010 Age and CBC differences as measured by kruskal walis and chisqr between the various genotype groups

## 
## 
##   * **g6pd_202_rtpcr**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          2.281        2    0.3196
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[, "age_at_collection_months_2010"]` by `x`
## 
##   * **thal**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          2.192        2    0.3342
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[, "age_at_collection_months_2010"]` by `x`
## 
##   * **sickle**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##         0.00716       1    0.9326
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[, "age_at_collection_months_2010"]` by `x`
## 
## 
## <!-- end of list -->
##  
##  minus uncomplicated malaria cases
## 
## 
##   * **g6pd_202_rtpcr**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          2.924        2    0.2318
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[pgd_genopheno_01042018[, "uncomplicated_malaria_2010"] == "NO" & !is.na(pgd_genopheno_01042018[, rbc_polymorphism]), "age_at_collection_months_2010"]` by `x`
## 
##   * **thal**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          2.759        2    0.2518
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[pgd_genopheno_01042018[, "uncomplicated_malaria_2010"] == "NO" & !is.na(pgd_genopheno_01042018[, rbc_polymorphism]), "age_at_collection_months_2010"]` by `x`
## 
##   * **sickle**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          0.1787       1    0.6725
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `pgd_genopheno_01042018[pgd_genopheno_01042018[, "uncomplicated_malaria_2010"] == "NO" & !is.na(pgd_genopheno_01042018[, rbc_polymorphism]), "age_at_collection_months_2010"]` by `x`
## 
## 
## <!-- end of list -->
## G6PD202 and Thal combination (contigency table)
## 
## -------------------------------
##  Test statistic   df   P value 
## ---------------- ---- ---------
##      5.866        4    0.2094  
## -------------------------------
## 
## Table: Pearson's Chi-squared test: `.`
## G6PD202 and sickle combination (contigency table)
## 
## -------------------------------
##  Test statistic   df   P value 
## ---------------- ---- ---------
##      0.4244       2    0.8088  
## -------------------------------
## 
## Table: Pearson's Chi-squared test: `.`
## Thal and sickle combination (contigency table)
## 
## -------------------------------
##  Test statistic   df   P value 
## ---------------- ---- ---------
##      0.1022       2    0.9502  
## -------------------------------
## 
## Table: Pearson's Chi-squared test: `.`
## G6PD202 and Thal combination (contigency table)
## 
## -------------------------------
##  Test statistic   df   P value 
## ---------------- ---- ---------
##      5.817        4    0.2132  
## -------------------------------
## 
## Table: Pearson's Chi-squared test: `.`
## G6PD202 and sickle combination (contigency table)
## 
## -------------------------------
##  Test statistic   df   P value 
## ---------------- ---- ---------
##     0.09709       2    0.9526  
## -------------------------------
## 
## Table: Pearson's Chi-squared test: `.`
## Thal and sickle combination (contigency table)
## 
## -------------------------------
##  Test statistic   df   P value 
## ---------------- ---- ---------
##      0.862        2    0.6498  
## -------------------------------
## 
## Table: Pearson's Chi-squared test: `.`
## association between malaria status and rbc polymorphism and cbc indices
## 
## 
##   * **g6pd_202_rtpcr**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          0.9557       4    0.9164
##     -------------------------------
## 
##     Table: Pearson's Chi-squared test: `pgd_genopheno_01042018$malaria_status` and `x`
## 
##   * **thal**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          1.784        4    0.7755
##     -------------------------------
## 
##     Table: Pearson's Chi-squared test: `pgd_genopheno_01042018$malaria_status` and `x`
## 
##   * **sickle**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          2.523        2    0.2832
##     -------------------------------
## 
##     Table: Pearson's Chi-squared test: `pgd_genopheno_01042018$malaria_status` and `x`
## 
## 
## <!-- end of list -->
## 
## 
##   * **rbc_2010**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          1.106        2    0.5753
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
## 
##   * **hgb_2010**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          1.513        2    0.4692
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
## 
##   * **mcv_2010**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          1.137        2    0.5664
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
## 
##   * **mch_2010**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##           1.35        2    0.5092
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
## 
##   * **mchc_2010**:
## 
##     -------------------------------
##      Test statistic   df   P value
##     ---------------- ---- ---------
##          3.371        2    0.1853
##     -------------------------------
## 
##     Table: Kruskal-Wallis rank sum test: `x` by `pgd_genopheno_01042018[, "malaria_status"]`
## 
## 
## <!-- end of list -->

##Univariate association tests (each cbc trait versus each polymorphism)

g6pd_202_rtpcr ++++ g6pd_202_rtpcr

rbc_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 0.003601 NA
    HOM/HEMI 2.306e-05 0.2444

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.2096 -0.3666 -0.05261 0.005112
    HOM/HEMI-NORM -0.3107 -0.4722 -0.1492 2.288e-05
    HOM/HEMI-HET -0.1011 -0.3051 0.1029 0.4744

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
27.93 2 8.596e-07 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
14.68 2 141.7 1.61e-06 * * *

hgb_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 0.8898 NA
    HOM/HEMI 0.8898 0.8898

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.03899 -0.3932 0.3153 0.9638
    HOM/HEMI-NORM 0.162 -0.2025 0.5264 0.5489
    HOM/HEMI-HET 0.201 -0.2594 0.6613 0.5606

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.6951 2 0.7064

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
0.8219 2 139.4 0.4417

mcv_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 0.003899 NA
    HOM/HEMI 1.315e-09 0.00836

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM 3.077 0.7548 5.399 0.005524
    HOM/HEMI-NORM 6.476 4.087 8.865 1.345e-09
    HOM/HEMI-HET 3.399 0.3814 6.417 0.02272

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
44.47 2 2.201e-10 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
23.42 2 137 1.786e-09 * * *

mch_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 0.03751 NA
    HOM/HEMI 2.928e-06 0.03751

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM 0.9323 0.002903 1.862 0.04909
    HOM/HEMI-NORM 2.018 1.062 2.974 2.916e-06
    HOM/HEMI-HET 1.085 -0.1224 2.293 0.08829

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
29.07 2 4.872e-07 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
13.85 2 135.3 3.37e-06 * * *

mchc_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 0.7471 NA
    HOM/HEMI 0.7167 0.8042

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.1232 -0.4482 0.2019 0.6464
    HOM/HEMI-NORM -0.1677 -0.5021 0.1667 0.4662
    HOM/HEMI-HET -0.04457 -0.467 0.3779 0.9667

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
1.423 2 0.4909

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
0.9746 2 133.1 0.38

thal ++++ thal

rbc_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 9.891e-05 NA
    HOM 4.129e-27 1.766e-17

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM 0.1937 0.07774 0.3097 0.000291
    HOM-NORM 0.7038 0.5613 0.8463 3.061e-11
    HOM-HET 0.5101 0.3759 0.6442 3.064e-11

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
113.4 2 2.36e-25 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
83.62 2 266.3 6.578e-29 * * *

hgb_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 0.04803 NA
    HOM 6.386e-05 0.009167

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.2399 -0.5245 0.04464 0.1177
    HOM-NORM -0.6387 -0.9884 -0.289 6.314e-05
    HOM-HET -0.3987 -0.7279 -0.06963 0.01271

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
19.4 2 6.134e-05 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
10.47 2 263.1 4.231e-05 * * *

mcv_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 1.58e-08 NA
    HOM 2.329e-38 5.189e-22

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -4.056 -5.714 -2.398 4.739e-08
    HOM-NORM -12.4 -14.43 -10.36 3.061e-11
    HOM-HET -8.339 -10.26 -6.422 3.064e-11

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
160.2 2 1.603e-35 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
135.9 2 270.6 1.382e-41 * * *

mch_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 1.091e-09 NA
    HOM 7.195e-41 9.246e-23

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -1.705 -2.349 -1.06 3.302e-09
    HOM-NORM -5.01 -5.802 -4.219 3.061e-11
    HOM-HET -3.306 -4.051 -2.56 3.064e-11

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
169.6 2 1.494e-37 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
156.9 2 277.8 2.478e-46 * * *

mchc_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM HET
    HET 1.719e-05 NA
    HOM 2.09e-20 1.251e-10

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.4483 -0.6909 -0.2056 5.103e-05
    HOM-NORM -1.247 -1.545 -0.9488 3.064e-11
    HOM-HET -0.7987 -1.079 -0.5181 2.183e-10

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
88.44 2 6.254e-20 * * *

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
54.4 2 261.6 1.756e-20 * * *

sickle ++++ sickle

rbc_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM
    HET 0.4387

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM 0.05139 -0.07888 0.1817 0.4387

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
1.039 1 0.3082

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
0.6029 1 109.5 0.4391

hgb_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM
    HET 0.7687

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.04293 -0.3297 0.2438 0.7687

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.227 1 0.6338

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
0.08677 1 109.4 0.7689

mcv_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM
    HET 0.5093

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.6597 -2.623 1.303 0.5093

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.6876 1 0.407

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
0.4791 1 114.5 0.4902

mch_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM
    HET 0.3043

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.4037 -1.175 0.3678 0.3043

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
1.677 1 0.1953

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
1.172 1 115 0.2812

mchc_2010

pairwise t.test_____________________________________________________________

  • method: t tests with pooled SD

  • data.name: b[, cbc_array[j]] and b[, rbc_polymorphism[i]]

  • p.value:

      NORM
    HET 0.07142

  • p.adjust.method: holm

tukeyHSD____________________________________________________________________

  • b[, rbc_polymorphism[i]]:

      diff lwr upr p adj
    HET-NORM -0.2413 -0.5037 0.02112 0.07142

kruskal.test________________________________________________________________

Kruskal-Wallis rank sum test: b[, cbc_array[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
3.612 1 0.05738

Oneway anova________________________________________________________________

One-way analysis of means (not assuming equal variances): b[, cbc_array[j]] and b[, rbc_polymorphism[i]]
Test statistic num df denom df P value
3.901 1 119.9 0.05055

##Univariate association of CBC traits with sex, malaria and age {.tabset}

Testing for the proportion of variability in the RBC CBC indices that is accounted for by different measured factors

rbc_2010

lm__________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.617 0.06198 74.49 8.701e-264 * * *
age_at_collection_years_2010 0.00449 0.007502 0.5985 0.5498
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5355 0.0007552 -0.001353
  2.5 % 97.5 %
(Intercept) 4.495 4.739
age_at_collection_years_2010 -0.01025 0.01923
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.648 0.0351 132.4 0 * * *
sexMALE 0.00559 0.04912 0.1138 0.9094
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5357 2.733e-05 -0.002082
  2.5 % 97.5 %
(Intercept) 4.579 4.717
sexMALE -0.09093 0.1021
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.662 0.02754 169.3 0 * * *
malaria_statusassymptomatic_malaria -0.01615 0.0755 -0.2139 0.8307
malaria_statusuncomplicated_malaria -0.1116 0.08902 -1.254 0.2106
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5354 0.003323 -0.0008912
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.595 0.0443 103.7 2.385e-297 * * *
ethnicDigo -0.2595 0.3836 -0.6765 0.4991
ethnicDurum -0.1695 0.3836 -0.4419 0.6588
ethnicGiriama 0.05027 0.05745 0.8749 0.3821
ethnicJibana 0.0724 0.097 0.7463 0.4559
ethnicKambe 0.9805 0.3836 2.556 0.01095 *
ethnicKauma 0.253 0.1956 1.293 0.1967
ethnicRabai -0.1995 0.3836 -0.5201 0.6033
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
420 0.5389 0.02298 0.006378

hgb_2010

lm__________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.2 0.1258 81.04 7.072e-280 * * *
age_at_collection_years_2010 0.1388 0.01523 9.116 2.208e-18 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.087 0.1492 0.1474
  2.5 % 97.5 %
(Intercept) 9.949 10.44
age_at_collection_years_2010 0.1089 0.1688
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.21 0.07717 145.3 0 * * *
sexMALE 0.0744 0.108 0.6888 0.4913
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.178 0.001 -0.001108
  2.5 % 97.5 %
(Intercept) 11.06 11.36
sexMALE -0.1378 0.2866
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.27 0.06062 185.9 0 * * *
malaria_statusassymptomatic_malaria -0.1631 0.1662 -0.9812 0.327
malaria_statusuncomplicated_malaria 0.01925 0.196 0.09821 0.9218
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.179 0.002124 -0.002095
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.33 0.09416 120.3 3.953e-323 * * *
ethnicDigo -0.7318 0.8154 -0.8974 0.37
ethnicDurum -1.482 0.8154 -1.817 0.06993
ethnicGiriama 0.03691 0.1221 0.3022 0.7626
ethnicJibana -0.3343 0.2062 -1.621 0.1057
ethnicKambe 0.7682 0.8154 0.9421 0.3467
ethnicKauma -0.8193 0.4158 -1.97 0.04947 *
ethnicRabai -0.4818 0.8154 -0.5908 0.555
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
420 1.146 0.02973 0.01324

mcv_2010

lm__________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.45 0.8984 76.19 4.356e-268 * * *
age_at_collection_years_2010 0.6749 0.1087 6.207 1.179e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 7.762 0.07517 0.07322
  2.5 % 97.5 %
(Intercept) 66.68 70.21
age_at_collection_years_2010 0.4613 0.8886
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.28 0.5284 138.7 0 * * *
sexMALE 0.5675 0.7396 0.7673 0.4433
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 8.066 0.001241 -0.0008665
  2.5 % 97.5 %
(Intercept) 72.24 74.32
sexMALE -0.8858 2.021
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.5 0.4151 177.1 0 * * *
malaria_statusassymptomatic_malaria -0.2909 1.138 -0.2556 0.7984
malaria_statusuncomplicated_malaria 1.296 1.342 0.9662 0.3345
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 8.071 0.002253 -0.001966
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.7 0.6468 115.5 5.942e-316 * * *
ethnicDigo -1.048 5.601 -0.1871 0.8517
ethnicDurum -6.698 5.601 -1.196 0.2325
ethnicGiriama -0.3123 0.8388 -0.3723 0.7099
ethnicJibana -2.603 1.416 -1.838 0.06678
ethnicKambe -7.198 5.601 -1.285 0.1995
ethnicKauma -6.848 2.856 -2.398 0.01694 *
ethnicRabai -3.698 5.601 -0.6602 0.5095
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
420 7.868 0.02748 0.01096

mch_2010

lm__________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.42 0.3534 63.44 9.416e-234 * * *
age_at_collection_years_2010 0.2647 0.04277 6.188 1.318e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.053 0.07475 0.0728
  2.5 % 97.5 %
(Intercept) 21.72 23.11
age_at_collection_years_2010 0.1806 0.3487
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.31 0.2078 117 0 * * *
sexMALE 0.2385 0.2908 0.8199 0.4127
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.172 0.001416 -0.0006906
  2.5 % 97.5 %
(Intercept) 23.9 24.71
sexMALE -0.333 0.81
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.4 0.1632 149.6 0 * * *
malaria_statusassymptomatic_malaria -0.2062 0.4474 -0.4609 0.6451
malaria_statusuncomplicated_malaria 0.5705 0.5275 1.082 0.28
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.173 0.00319 -0.001024
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.89 0.2543 97.86 2.394e-287 * * *
ethnicDigo -0.4365 2.202 -0.1982 0.843
ethnicDurum -2.686 2.202 -1.22 0.2232
ethnicGiriama -0.1708 0.3298 -0.5179 0.6048
ethnicJibana -1.069 0.5569 -1.919 0.0557
ethnicKambe -3.236 2.202 -1.47 0.1425
ethnicKauma -2.761 1.123 -2.459 0.01434 *
ethnicRabai -0.08649 2.202 -0.03927 0.9687
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
420 3.094 0.02878 0.01228

mchc_2010

lm__________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.65 0.1229 265.7 0 * * *
age_at_collection_years_2010 0.06427 0.01487 4.322 1.886e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[4]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.061 0.03791 0.03588
  2.5 % 97.5 %
(Intercept) 32.41 32.89
age_at_collection_years_2010 0.03505 0.09349
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.1 0.07086 467.1 0 * * *
sexMALE 0.07293 0.09917 0.7354 0.4625
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[5]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.082 0.00114 -0.0009677
  2.5 % 97.5 %
(Intercept) 32.96 33.24
sexMALE -0.1219 0.2678
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.13 0.05552 596.8 0 * * *
malaria_statusassymptomatic_malaria -0.1592 0.1522 -1.046 0.2962
malaria_statusuncomplicated_malaria 0.2517 0.1795 1.402 0.1615
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[6]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.079 0.007209 0.003011
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.25 0.08759 379.6 0 * * *
ethnicDigo -0.001351 0.7585 -0.001782 0.9986
ethnicDurum -0.5014 0.7585 -0.661 0.509
ethnicGiriama -0.08269 0.1136 -0.7279 0.4671
ethnicJibana -0.277 0.1918 -1.444 0.1494
ethnicKambe -1.201 0.7585 -1.584 0.114
ethnicKauma -0.7764 0.3868 -2.007 0.04538 *
ethnicRabai 1.549 0.7585 2.042 0.04182 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, colnames(b)[7]))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
420 1.066 0.03011 0.01363

##Multivariate wambua {.tabset}

rbc_2010

####All_vs_g6pd+thal________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.48 0.03921 114.2 0 * * *
g6pd_202_rtpcrHET -0.1807 0.05911 -3.057 0.00236 * *
g6pd_202_rtpcrHOM/HEMI -0.272 0.06085 -4.47 9.8e-06 * * *
thalHET 0.2082 0.04827 4.314 1.956e-05 * * *
thalHOM 0.6882 0.05928 11.61 1.413e-27 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.4607 0.2652 0.259
  2.5 % 97.5 %
(Intercept) 4.403 4.557
g6pd_202_rtpcrHET -0.2969 -0.06456
g6pd_202_rtpcrHOM/HEMI -0.3916 -0.1524
thalHET 0.1134 0.3031
thalHOM 0.5717 0.8047
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 4.479685 0.0392144 471 4.402628 4.556742
  • HET NORM 4.298978 0.0617492 471 4.177640 4.420316
  • HOM/HEMI NORM 4.207659 0.0632430 471 4.083385 4.331932
  • NORM HET 4.687897 0.0350109 471 4.619100 4.756694
  • HET HET 4.507190 0.0567910 471 4.395595 4.618785
  • HOM/HEMI HET 4.415871 0.0584286 471 4.301058 4.530684
  • NORM HOM 5.167866 0.0474255 471 5.074674 5.261058
  • HET HOM 4.987159 0.0689998 471 4.851573 5.122745
  • HOM/HEMI HOM 4.895840 0.0710101 471 4.756304 5.035376
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.475 0.06117 73.17 5.091e-259 * * *
g6pd_202_rtpcrHET -0.1806 0.05919 -3.051 0.002412 * *
g6pd_202_rtpcrHOM/HEMI -0.2723 0.06101 -4.464 1.01e-05 * * *
thalHET 0.208 0.04839 4.298 2.096e-05 * * *
thalHOM 0.6878 0.05949 11.56 2.208e-27 * * *
age_at_collection_years_2010 0.000587 0.006493 0.09041 0.928
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.4612 0.2652 0.2574
  2.5 % 97.5 %
(Intercept) 4.355 4.596
g6pd_202_rtpcrHET -0.2969 -0.06426
g6pd_202_rtpcrHOM/HEMI -0.3922 -0.1524
thalHET 0.1129 0.3031
thalHOM 0.5709 0.8047
age_at_collection_years_2010 -0.01217 0.01335
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 4.479897 0.0393260 470 4.402621 4.557174
  • HET NORM 4.299329 0.0619365 470 4.177623 4.421036
  • HOM/HEMI NORM 4.207563 0.0633186 470 4.083140 4.331986
  • NORM HET 4.687873 0.0350488 470 4.619001 4.756744
  • HET HET 4.507305 0.0568650 470 4.395563 4.619046
  • HOM/HEMI HET 4.415538 0.0586060 470 4.300376 4.530700
  • NORM HOM 5.167699 0.0475115 470 5.074338 5.261060
  • HET HOM 4.987131 0.0690733 470 4.851400 5.122862
  • HOM/HEMI HOM 4.895365 0.0712792 470 4.755299 5.035430
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

####All_vs_g6pd+sickle________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.72 0.03045 155 0 * * *
g6pd_202_rtpcrHET -0.2114 0.06681 -3.164 0.001659 * *
g6pd_202_rtpcrHOM/HEMI -0.3116 0.0687 -4.535 7.308e-06 * * *
sickleHET 0.05946 0.06469 0.9191 0.3585
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5222 0.05391 0.0479
  2.5 % 97.5 %
(Intercept) 4.661 4.78
g6pd_202_rtpcrHET -0.3426 -0.08008
g6pd_202_rtpcrHOM/HEMI -0.4466 -0.1766
sickleHET -0.06766 0.1866
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 4.720387 0.0304483 472 4.660556 4.780218
  • HET NORM 4.509035 0.0614947 472 4.388197 4.629872
  • HOM/HEMI NORM 4.408807 0.0633914 472 4.284243 4.533372
  • NORM HET 4.779845 0.0616202 472 4.658761 4.900929
  • HET HET 4.568492 0.0800255 472 4.411242 4.725742
  • HOM/HEMI HET 4.468265 0.0822708 472 4.306602 4.629927
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.678 0.06319 74.02 1.497e-261 * * *
g6pd_202_rtpcrHET -0.21 0.06686 -3.14 0.001793 * *
g6pd_202_rtpcrHOM/HEMI -0.3145 0.06884 -4.569 6.277e-06 * * *
sickleHET 0.05942 0.06472 0.9181 0.359
age_at_collection_years_2010 0.005673 0.007334 0.7735 0.4396
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5224 0.05511 0.04709
  2.5 % 97.5 %
(Intercept) 4.553 4.802
g6pd_202_rtpcrHET -0.3414 -0.07859
g6pd_202_rtpcrHOM/HEMI -0.4497 -0.1792
sickleHET -0.06775 0.1866
age_at_collection_years_2010 -0.008739 0.02009
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 4.720604 0.0304626 471 4.660744 4.780463
  • HET NORM 4.510631 0.0615555 471 4.389673 4.631588
  • HOM/HEMI NORM 4.406120 0.0635135 471 4.281315 4.530925
  • NORM HET 4.780021 0.0616469 471 4.658884 4.901158
  • HET HET 4.570048 0.0800849 471 4.412680 4.727416
  • HOM/HEMI HET 4.465537 0.0823814 471 4.303656 4.627417
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.73 0.0287 164.8 0 * * *
g6pd_202_rtpcrHET -0.2096 0.06677 -3.139 0.001801 * *
g6pd_202_rtpcrHOM/HEMI -0.3107 0.06869 -4.524 7.687e-06 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5221 0.05222 0.04821
  2.5 % 97.5 %
(Intercept) 4.673 4.786
g6pd_202_rtpcrHET -0.3408 -0.07839
g6pd_202_rtpcrHOM/HEMI -0.4457 -0.1758
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.687 0.06236 75.15 9.145e-265 * * *
g6pd_202_rtpcrHET -0.2082 0.06682 -3.116 0.001945 * *
g6pd_202_rtpcrHOM/HEMI -0.3136 0.06882 -4.557 6.602e-06 * * *
age_at_collection_years_2010 0.005678 0.007333 0.7743 0.4391
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5223 0.05342 0.0474
  2.5 % 97.5 %
(Intercept) 4.564 4.809
g6pd_202_rtpcrHET -0.3395 -0.07691
g6pd_202_rtpcrHOM/HEMI -0.4489 -0.1784
age_at_collection_years_2010 -0.008731 0.02009

g6pd_202_rtpcr _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.449 0.07489 59.4 2.902e-177 * * *
thalHET 0.2765 0.06105 4.53 8.287e-06 * * *
thalHOM 0.7031 0.07163 9.816 4.2e-20 * * *
age_at_collection_years_2010 -0.000304 0.008092 -0.03757 0.9701
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 0.4823 0.2281 0.221
  2.5 % 97.5 %
(Intercept) 4.302 4.596
thalHET 0.1564 0.3966
thalHOM 0.5622 0.844
age_at_collection_years_2010 -0.01622 0.01561
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.446688 0.0458142 327 4.356561 4.536816
  • HET 4.723231 0.0403339 327 4.643884 4.802577
  • HOM 5.149813 0.0549976 327 5.041619 5.258007
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.447 0.04571 97.29 5.604e-244 * * *
thalHET 0.2765 0.06092 4.538 7.972e-06 * * *
thalHOM 0.703 0.07142 9.843 3.374e-20 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 0.4816 0.2281 0.2234
  2.5 % 97.5 %
(Intercept) 4.357 4.537
thalHET 0.1566 0.3963
thalHOM 0.5625 0.8435
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.446757 0.0457085 328 4.356838 4.536676
  • HET 4.723217 0.0402708 328 4.643995 4.802438
  • HOM 5.149740 0.0548798 328 5.041779 5.257701
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.694 0.07617 61.63 2.089e-182 * * *
sickleHET 0.05799 0.08279 0.7005 0.4841
age_at_collection_years_2010 0.003548 0.009183 0.3864 0.6994
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 0.5476 0.002024 -0.004061
  2.5 % 97.5 %
(Intercept) 4.544 4.844
sickleHET -0.1049 0.2209
age_at_collection_years_2010 -0.01452 0.02161
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.720617 0.0327871 328 4.656118 4.785117
  • HET 4.778612 0.0759970 328 4.629109 4.928115
sickle n
NORM 279
HET 52

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.72 0.03274 144.2 2.106e-299 * * *
sickleHET 0.05941 0.0826 0.7193 0.4725
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 0.5469 0.00157 -0.001465
  2.5 % 97.5 %
(Intercept) 4.656 4.785
sickleHET -0.1031 0.2219
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.720394 0.0327396 329 4.655989 4.784800
  • HET 4.779808 0.0758357 329 4.630624 4.928992
sickle n
NORM 279
HET 52

g6pd_202_rtpcr _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.46 0.1236 36.09 2.043e-47 * * *
thalHET 0.07402 0.1011 0.7322 0.4665
thalHOM 0.7263 0.1408 5.157 2.178e-06 * * *
age_at_collection_years_2010 -0.01309 0.01397 -0.9373 0.3518
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 0.3856 0.2991 0.2694
  2.5 % 97.5 %
(Intercept) 4.214 4.707
thalHET -0.1276 0.2756
thalHOM 0.4455 1.007
age_at_collection_years_2010 -0.04095 0.01476
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.364445 0.0808341 71 4.203267 4.525624
  • HET 4.438467 0.0610143 71 4.316808 4.560126
  • HOM 5.090755 0.1136838 71 4.864076 5.317434
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.372 0.08034 54.42 3.103e-60 * * *
thalHET 0.06833 0.1008 0.6776 0.5002
thalHOM 0.697 0.1372 5.08 2.867e-06 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 0.3853 0.2904 0.2707
  2.5 % 97.5 %
(Intercept) 4.212 4.532
thalHET -0.1327 0.2693
thalHOM 0.4235 0.9705
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.372174 0.0803445 72 4.21201 4.532338
  • HET 4.440500 0.0609242 72 4.31905 4.561950
  • HOM 5.069167 0.1112319 72 4.84743 5.290903
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.537 0.1356 33.46 1.292e-45 * * *
sickleHET -0.1458 0.1362 -1.07 0.288
age_at_collection_years_2010 0.001428 0.01621 0.08811 0.93
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 0.4536 0.0166 -0.01072
  2.5 % 97.5 %
(Intercept) 4.267 4.807
sickleHET -0.4173 0.1257
age_at_collection_years_2010 -0.03089 0.03375
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.547345 0.0582211 72 4.431283 4.663407
  • HET 4.401568 0.1225292 72 4.157311 4.645825
sickle n
NORM 61
HET 14

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.548 0.05768 78.84 2.16e-72 * * *
sickleHET -0.1477 0.1335 -1.106 0.2722
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 0.4505 0.01649 0.003018
  2.5 % 97.5 %
(Intercept) 4.433 4.663
sickleHET -0.4138 0.1184
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.547705 0.0576816 73 4.432746 4.662664
  • HET 4.400000 0.1204033 73 4.160037 4.639963
sickle n
NORM 61
HET 14

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.137 0.1514 27.32 1.067e-37 * * *
thalHET 0.03443 0.1146 0.3004 0.7648
thalHOM 0.6414 0.1616 3.97 0.0001798 * * *
age_at_collection_years_2010 0.02134 0.01614 1.322 0.1909
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.4236 0.2275 0.1924
  2.5 % 97.5 %
(Intercept) 3.834 4.439
thalHET -0.1944 0.2633
thalHOM 0.3189 0.964
age_at_collection_years_2010 -0.0109 0.05357
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.308677 0.0907195 66 4.127550 4.489804
  • HET 4.343109 0.0691337 66 4.205079 4.481139
  • HOM 4.950097 0.1343148 66 4.681929 5.218265
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.297 0.09081 47.32 3.229e-53 * * *
thalHET 0.05589 0.1141 0.4898 0.6259
thalHOM 0.6397 0.1624 3.938 0.0001982 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.4259 0.2071 0.1834
  2.5 % 97.5 %
(Intercept) 4.116 4.479
thalHET -0.1719 0.2836
thalHOM 0.3155 0.964
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.297273 0.0908100 67 4.116015 4.478530
  • HET 4.353158 0.0690960 67 4.215242 4.491074
  • HOM 4.937000 0.1346929 67 4.668152 5.205848
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.228 0.1528 27.66 2.231e-38 * * *
sickleHET 0.2999 0.1462 2.051 0.04423 *
age_at_collection_years_2010 0.01736 0.01734 1.001 0.3205
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.461 0.07106 0.04333
  2.5 % 97.5 %
(Intercept) 3.923 4.533
sickleHET 0.007972 0.5917
age_at_collection_years_2010 -0.01726 0.05197
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.367596 0.0605365 67 4.246765 4.488427
  • HET 4.667453 0.1331065 67 4.401771 4.933135
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.368 0.06054 72.16 5.459e-66 * * *
sickleHET 0.2969 0.1462 2.031 0.0462 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.461 0.05717 0.04331
  2.5 % 97.5 %
(Intercept) 4.247 4.489
sickleHET 0.005147 0.5886
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.368104 0.0605351 68 4.247307 4.488899
  • HET 4.665000 0.1330855 68 4.399432 4.930568
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.415 0.03778 116.9 0 * * *
thalHET 0.1937 0.04934 3.927 9.891e-05 * * *
thalHOM 0.7038 0.06063 11.61 1.376e-27 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.4718 0.226 0.2228
  2.5 % 97.5 %
(Intercept) 4.34 4.489
thalHET 0.09679 0.2907
thalHOM 0.5847 0.8229
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.418 0.06126 72.13 5.167e-257 * * *
thalHET 0.1939 0.04947 3.921 0.0001014 * * *
thalHOM 0.7041 0.06083 11.57 1.915e-27 * * *
age_at_collection_years_2010 -0.0004891 0.006634 -0.07372 0.9413
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.4723 0.226 0.2211
  2.5 % 97.5 %
(Intercept) 4.298 4.539
thalHET 0.09674 0.2911
thalHOM 0.5846 0.8237
age_at_collection_years_2010 -0.01353 0.01255

thal _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.535 0.1004 45.15 5.726e-90 * * *
g6pd_202_rtpcrHET -0.0819 0.114 -0.7188 0.4734
g6pd_202_rtpcrHOM/HEMI -0.1471 0.1159 -1.269 0.2063
age_at_collection_years_2010 -0.01202 0.01211 -0.9931 0.3223
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.4963 0.0184 -0.0009723
  2.5 % 97.5 %
(Intercept) 4.336 4.733
g6pd_202_rtpcrHET -0.307 0.1432
g6pd_202_rtpcrHOM/HEMI -0.376 0.08185
age_at_collection_years_2010 -0.03595 0.0119
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.447494 0.0471165 152 4.354406 4.540581
  • HET 4.365590 0.1037064 152 4.160698 4.570482
  • HOM/HEMI 4.300438 0.1058683 152 4.091274 4.509601
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.447 0.04711 94.39 1.619e-137 * * *
g6pd_202_rtpcrHET -0.07458 0.1137 -0.6559 0.5129
g6pd_202_rtpcrHOM/HEMI -0.1495 0.1158 -1.291 0.1988
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.4963 0.01203 -0.0008819
  2.5 % 97.5 %
(Intercept) 4.354 4.54
g6pd_202_rtpcrHET -0.2992 0.1501
g6pd_202_rtpcrHOM/HEMI -0.3783 0.07934
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.446757 0.0471085 153 4.353690 4.539824
  • HET 4.372174 0.1034896 153 4.167721 4.576627
  • HOM/HEMI 4.297273 0.1058155 153 4.088225 4.506321
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.488 0.0985 45.56 6.665e-91 * * *
sickleHET 0.07205 0.1068 0.6745 0.501
age_at_collection_years_2010 -0.01171 0.0121 -0.9679 0.3346
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.497 0.009236 -0.003716
  2.5 % 97.5 %
(Intercept) 4.293 4.682
sickleHET -0.139 0.2831
age_at_collection_years_2010 -0.03561 0.01219
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.402671 0.0435940 153 4.316547 4.488795
  • HET 4.474721 0.0975004 153 4.282100 4.667342
sickle n
NORM 130
HET 26

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.402 0.04358 101 1.195e-142 * * *
sickleHET 0.07469 0.1068 0.6997 0.4852
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.4969 0.003169 -0.003304
  2.5 % 97.5 %
(Intercept) 4.316 4.488
sickleHET -0.1362 0.2856
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.402231 0.0435827 154 4.316134 4.488328
  • HET 4.476923 0.0974539 154 4.284404 4.669442
sickle n
NORM 130
HET 26

thal _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.698 0.08049 58.37 1.192e-134 * * *
g6pd_202_rtpcrHET -0.2813 0.08239 -3.414 0.0007636 * * *
g6pd_202_rtpcrHOM/HEMI -0.3731 0.08441 -4.42 1.557e-05 * * *
age_at_collection_years_2010 0.003257 0.009309 0.3498 0.7268
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 0.46 0.1072 0.09487
  2.5 % 97.5 %
(Intercept) 4.54 4.857
g6pd_202_rtpcrHET -0.4437 -0.1189
g6pd_202_rtpcrHOM/HEMI -0.5395 -0.2067
age_at_collection_years_2010 -0.01509 0.0216
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.723480 0.0384763 217 4.647645 4.799315
  • HET 4.442209 0.0728996 217 4.298527 4.585891
  • HOM/HEMI 4.350368 0.0750501 217 4.202448 4.498289
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.723 0.03839 123 2.134e-203 * * *
g6pd_202_rtpcrHET -0.2827 0.08212 -3.443 0.00069 * * *
g6pd_202_rtpcrHOM/HEMI -0.3701 0.08379 -4.417 1.579e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 0.4591 0.1067 0.09851
  2.5 % 97.5 %
(Intercept) 4.648 4.799
g6pd_202_rtpcrHET -0.4446 -0.1209
g6pd_202_rtpcrHOM/HEMI -0.5352 -0.2049
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.723217 0.0383914 218 4.647551 4.798883
  • HET 4.440500 0.0725892 218 4.297434 4.583566
  • HOM/HEMI 4.353158 0.0744749 218 4.206375 4.499941
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.592 0.08187 56.1 1.585e-131 * * *
sickleHET 0.1055 0.08945 1.179 0.2396
age_at_collection_years_2010 -9.892e-05 0.009745 -0.01015 0.9919
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 0.4842 0.006365 -0.002751
  2.5 % 97.5 %
(Intercept) 4.431 4.754
sickleHET -0.07081 0.2818
age_at_collection_years_2010 -0.01931 0.01911
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.591710 0.0355178 218 4.521708 4.661712
  • HET 4.697199 0.0820286 218 4.535528 4.858869
sickle n
NORM 186
HET 35

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.592 0.03542 129.6 5.174e-209 * * *
sickleHET 0.1054 0.08901 1.184 0.2375
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 0.4831 0.006365 0.001828
  2.5 % 97.5 %
(Intercept) 4.522 4.662
sickleHET -0.07 0.2808
  • sickle emmean SE df lower.CL upper.CL
  • NORM 4.591720 0.0354215 219 4.52191 4.661531
  • HET 4.697143 0.0816563 219 4.53621 4.858076
sickle n
NORM 186
HET 35

thal _ HOM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.009 0.1137 44.07 4.821e-65 * * *
g6pd_202_rtpcrHET -0.1016 0.1243 -0.8178 0.4155
g6pd_202_rtpcrHOM/HEMI -0.2066 0.1336 -1.546 0.1253
age_at_collection_years_2010 0.01804 0.01339 1.348 0.181
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.3972 0.04585 0.01572
  2.5 % 97.5 %
(Intercept) 4.784 5.235
g6pd_202_rtpcrHET -0.3483 0.1451
g6pd_202_rtpcrHOM/HEMI -0.4718 0.05863
age_at_collection_years_2010 -0.008536 0.04461
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 5.151670 0.0452885 95 5.061761 5.241579
  • HET 5.050052 0.1155376 95 4.820681 5.279423
  • HOM/HEMI 4.945079 0.1257506 95 4.695433 5.194726
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.15 0.04546 113.3 5.057e-104 * * *
g6pd_202_rtpcrHET -0.08057 0.1238 -0.6508 0.5167
g6pd_202_rtpcrHOM/HEMI -0.2127 0.1341 -1.587 0.1159
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.3989 0.02761 0.007356
  2.5 % 97.5 %
(Intercept) 5.06 5.24
g6pd_202_rtpcrHET -0.3263 0.1652
g6pd_202_rtpcrHOM/HEMI -0.4789 0.05341
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 5.149740 0.0454578 96 5.059507 5.239973
  • HET 5.069167 0.1151497 96 4.840596 5.297737
  • HOM/HEMI 4.937000 0.1261402 96 4.686614 5.187386
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.01 0.1171 42.79 2.469e-64 * * *
sickleHET -0.1031 0.1072 -0.9615 0.3387
age_at_collection_years_2010 0.01595 0.01344 1.187 0.238
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.399 0.02711 0.006845
  2.5 % 97.5 %
(Intercept) 4.778 5.243
sickleHET -0.316 0.1097
age_at_collection_years_2010 -0.01072 0.04262
  • sickle emmean SE df lower.CL upper.CL
  • NORM 5.136191 0.0441260 96 5.048601 5.223780
  • HET 5.033081 0.0974523 96 4.839640 5.226522
sickle n
NORM 82
HET 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.139 0.04415 116.4 5.298e-106 * * *
sickleHET -0.1196 0.1066 -1.123 0.2644
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.3998 0.01282 0.002647
  2.5 % 97.5 %
(Intercept) 5.051 5.227
sickleHET -0.3311 0.09187
  • sickle emmean SE df lower.CL upper.CL
  • NORM 5.139024 0.0441544 97 5.051390 5.226659
  • HET 5.019412 0.0969743 97 4.826945 5.211879
sickle n
NORM 82
HET 17

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.643 0.02684 173 0 * * *
sickleHET 0.05139 0.0663 0.7751 0.4387
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5354 0.001266 -0.0008411
  2.5 % 97.5 %
(Intercept) 4.59 4.695
sickleHET -0.07888 0.1817
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.609 0.06295 73.21 3.64e-260 * * *
sickleHET 0.05136 0.06634 0.7743 0.4392
age_at_collection_years_2010 0.004487 0.007505 0.5979 0.5502
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.5358 0.00202 -0.0022
  2.5 % 97.5 %
(Intercept) 4.485 4.732
sickleHET -0.07899 0.1817
age_at_collection_years_2010 -0.01026 0.01924

sickle _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.657 0.06723 69.26 1.059e-222 * * *
g6pd_202_rtpcrHET -0.1733 0.07365 -2.353 0.01912 *
g6pd_202_rtpcrHOM/HEMI -0.3574 0.07535 -4.743 2.942e-06 * * *
age_at_collection_years_2010 0.008492 0.007955 1.068 0.2864
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 0.5211 0.06074 0.05358
  2.5 % 97.5 %
(Intercept) 4.525 4.789
g6pd_202_rtpcrHET -0.3181 -0.0285
g6pd_202_rtpcrHOM/HEMI -0.5056 -0.2093
age_at_collection_years_2010 -0.007148 0.02413
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.721235 0.0312057 394 4.659885 4.782585
  • HET 4.547937 0.0667167 394 4.416772 4.679102
  • HOM/HEMI 4.363815 0.0685379 394 4.229069 4.498560
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.72 0.0312 151.3 0 * * *
g6pd_202_rtpcrHET -0.1727 0.07366 -2.344 0.01956 *
g6pd_202_rtpcrHOM/HEMI -0.3523 0.07521 -4.684 3.874e-06 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 0.5212 0.05802 0.05325
  2.5 % 97.5 %
(Intercept) 4.659 4.782
g6pd_202_rtpcrHET -0.3175 -0.02787
g6pd_202_rtpcrHOM/HEMI -0.5002 -0.2044
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.720394 0.0312012 395 4.659053 4.781735
  • HET 4.547705 0.0667281 395 4.416518 4.678892
  • HOM/HEMI 4.368104 0.0684321 395 4.233567 4.502640
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.406 0.06621 66.55 2.051e-216 * * *
thalHET 0.1896 0.05335 3.555 0.0004241 * * *
thalHOM 0.7372 0.066 11.17 2.345e-25 * * *
age_at_collection_years_2010 -0.0005788 0.00713 -0.08118 0.9353
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 0.4664 0.2476 0.2419
  2.5 % 97.5 %
(Intercept) 4.276 4.537
thalHET 0.08476 0.2945
thalHOM 0.6075 0.867
age_at_collection_years_2010 -0.0146 0.01344
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.402066 0.0409529 394 4.321553 4.482580
  • HET 4.591711 0.0341955 394 4.524483 4.658940
  • HOM 5.139306 0.0516183 394 5.037825 5.240788
thal n
NORM 130
HET 186
HOM 82

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.402 0.04085 107.8 5.556e-295 * * *
thalHET 0.1895 0.05325 3.559 0.000418 * * *
thalHOM 0.7368 0.06568 11.22 1.549e-25 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 0.4658 0.2476 0.2438
  2.5 % 97.5 %
(Intercept) 4.322 4.483
thalHET 0.08481 0.2942
thalHOM 0.6077 0.8659
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.402231 0.0408512 395 4.321918 4.482544
  • HET 4.591720 0.0341523 395 4.524577 4.658863
  • HOM 5.139024 0.0514363 395 5.037901 5.240147
thal n
NORM 130
HET 186
HOM 82

sickle _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.889 0.1667 29.33 1.716e-42 * * *
g6pd_202_rtpcrHET -0.403 0.1606 -2.509 0.0143 *
g6pd_202_rtpcrHOM/HEMI -0.1143 0.1674 -0.6829 0.4968
age_at_collection_years_2010 -0.01382 0.01903 -0.7263 0.4699
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.5228 0.07951 0.0422
  2.5 % 97.5 %
(Intercept) 4.557 5.221
g6pd_202_rtpcrHET -0.723 -0.08295
g6pd_202_rtpcrHOM/HEMI -0.448 0.2193
age_at_collection_years_2010 -0.05174 0.02409
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.783899 0.0727201 74 4.639001 4.928797
  • HET 4.380901 0.1421813 74 4.097599 4.664204
  • HOM/HEMI 4.669554 0.1510545 74 4.368571 4.970537
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.78 0.07227 66.14 2.964e-68 * * *
g6pd_202_rtpcrHET -0.3798 0.1569 -2.42 0.01793 *
g6pd_202_rtpcrHOM/HEMI -0.1148 0.1669 -0.6879 0.4937
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.5212 0.07295 0.04823
  2.5 % 97.5 %
(Intercept) 4.636 4.924
g6pd_202_rtpcrHET -0.6924 -0.0672
g6pd_202_rtpcrHOM/HEMI -0.4473 0.2177
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 4.779808 0.0722730 75 4.635833 4.923783
  • HET 4.400000 0.1392880 75 4.122524 4.677476
  • HOM/HEMI 4.665000 0.1504482 75 4.365292 4.964708
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.509 0.1633 27.61 1.057e-40 * * *
thalHET 0.2255 0.1327 1.7 0.0934
thalHOM 0.5423 0.1578 3.437 0.0009671 * * *
age_at_collection_years_2010 -0.004556 0.01834 -0.2484 0.8045
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.5058 0.1384 0.1035
  2.5 % 97.5 %
(Intercept) 4.184 4.835
thalHET -0.03887 0.4899
thalHOM 0.228 0.8567
age_at_collection_years_2010 -0.0411 0.03199
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.474577 0.0996470 74 4.276026 4.673128
  • HET 4.700107 0.0863269 74 4.528097 4.872117
  • HOM 5.016898 0.1230949 74 4.771626 5.262170
thal n
NORM 26
HET 35
HOM 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.477 0.09858 45.42 2.588e-56 * * *
thalHET 0.2202 0.1301 1.692 0.09476
thalHOM 0.5425 0.1568 3.46 0.0008937 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.5026 0.1377 0.1147
  2.5 % 97.5 %
(Intercept) 4.281 4.673
thalHET -0.03903 0.4795
thalHOM 0.2302 0.8548
  • thal emmean SE df lower.CL upper.CL
  • NORM 4.476923 0.0985759 75 4.280550 4.673296
  • HET 4.697143 0.0849618 75 4.527890 4.866395
  • HOM 5.019412 0.1219082 75 4.776558 5.262265
thal n
NORM 26
HET 35
HOM 17

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

hgb_2010

####All_vs_g6pd+thal________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.48 0.09862 116.4 0 * * *
g6pd_202_rtpcrHET -0.0606 0.1486 -0.4077 0.6837
g6pd_202_rtpcrHOM/HEMI 0.1318 0.153 0.861 0.3897
thalHET -0.242 0.1214 -1.993 0.0468 *
thalHOM -0.635 0.1491 -4.259 2.477e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.159 0.03972 0.03157
  2.5 % 97.5 %
(Intercept) 11.29 11.68
g6pd_202_rtpcrHET -0.3527 0.2315
g6pd_202_rtpcrHOM/HEMI -0.169 0.4325
thalHET -0.4805 -0.003444
thalHOM -0.9279 -0.342
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 11.48458 0.0986231 471 11.29079 11.67838
  • HET NORM 11.42398 0.1552973 471 11.11882 11.72915
  • HOM/HEMI NORM 11.61636 0.1590544 471 11.30381 11.92890
  • NORM HET 11.24261 0.0880513 471 11.06959 11.41563
  • HET HET 11.18201 0.1428277 471 10.90135 11.46267
  • HOM/HEMI HET 11.37438 0.1469462 471 11.08563 11.66313
  • NORM HOM 10.84959 0.1192737 471 10.61522 11.08396
  • HET HOM 10.78899 0.1735325 471 10.44800 11.12999
  • HOM/HEMI HOM 10.98136 0.1785883 471 10.63044 11.33229
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.44 0.1406 74.31 6.107e-262 * * *
g6pd_202_rtpcrHET -0.02649 0.136 -0.1948 0.8457
g6pd_202_rtpcrHOM/HEMI 0.0562 0.1402 0.4008 0.6887
thalHET -0.3001 0.1112 -2.699 0.007206 * *
thalHOM -0.728 0.1367 -5.326 1.56e-07 * * *
age_at_collection_years_2010 0.1439 0.01492 9.645 3.313e-20 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.06 0.1984 0.1899
  2.5 % 97.5 %
(Intercept) 10.17 10.72
g6pd_202_rtpcrHET -0.2937 0.2408
g6pd_202_rtpcrHOM/HEMI -0.2193 0.3317
thalHET -0.5186 -0.0816
thalHOM -0.9966 -0.4594
age_at_collection_years_2010 0.1146 0.1732
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 11.53666 0.0903650 470 11.35909 11.71423
  • HET NORM 11.51017 0.1423202 470 11.23050 11.78983
  • HOM/HEMI NORM 11.59285 0.1454962 470 11.30695 11.87876
  • NORM HET 11.23656 0.0805367 470 11.07831 11.39482
  • HET HET 11.21007 0.1306668 470 10.95331 11.46684
  • HOM/HEMI HET 11.29276 0.1346674 470 11.02814 11.55739
  • NORM HOM 10.80862 0.1091738 470 10.59410 11.02315
  • HET HOM 10.78214 0.1587194 470 10.47025 11.09402
  • HOM/HEMI HOM 10.86482 0.1637883 470 10.54297 11.18667
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

####All_vs_g6pd+sickle________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.24 0.06877 163.4 0 * * *
g6pd_202_rtpcrHET -0.03771 0.1509 -0.25 0.8027
g6pd_202_rtpcrHOM/HEMI 0.1626 0.1552 1.048 0.2953
sickleHET -0.04326 0.1461 -0.2961 0.7673
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.179 0.002921 -0.003416
  2.5 % 97.5 %
(Intercept) 11.1 11.37
g6pd_202_rtpcrHET -0.3342 0.2588
g6pd_202_rtpcrHOM/HEMI -0.1423 0.4675
sickleHET -0.3304 0.2438
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 11.23912 0.0687663 472 11.10400 11.37425
  • HET NORM 11.20141 0.1388833 472 10.92850 11.47432
  • HOM/HEMI NORM 11.40170 0.1431668 472 11.12038 11.68303
  • NORM HET 11.19586 0.1391667 472 10.92240 11.46932
  • HET HET 11.15815 0.1807344 472 10.80300 11.51329
  • HOM/HEMI HET 11.35844 0.1858052 472 10.99333 11.72355
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.2 0.1318 77.33 8.022e-270 * * *
g6pd_202_rtpcrHET -0.004086 0.1395 -0.02929 0.9766
g6pd_202_rtpcrHOM/HEMI 0.09183 0.1436 0.6394 0.5229
sickleHET -0.04425 0.135 -0.3277 0.7433
age_at_collection_years_2010 0.1382 0.0153 9.033 4.324e-18 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.09 0.1501 0.1429
  2.5 % 97.5 %
(Intercept) 9.937 10.45
g6pd_202_rtpcrHET -0.2782 0.27
g6pd_202_rtpcrHOM/HEMI -0.1904 0.374
sickleHET -0.3096 0.2211
age_at_collection_years_2010 0.1082 0.1683
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 11.24439 0.0635570 471 11.11950 11.36928
  • HET NORM 11.24030 0.1284292 471 10.98794 11.49267
  • HOM/HEMI NORM 11.33622 0.1325143 471 11.07583 11.59661
  • NORM HET 11.20014 0.1286197 471 10.94740 11.45288
  • HET HET 11.19605 0.1670887 471 10.86772 11.52439
  • HOM/HEMI HET 11.29197 0.1718802 471 10.95422 11.62972
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.23 0.06476 173.4 0 * * *
g6pd_202_rtpcrHET -0.03899 0.1507 -0.2588 0.7959
g6pd_202_rtpcrHOM/HEMI 0.162 0.155 1.045 0.2966
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.178 0.002736 -0.001481
  2.5 % 97.5 %
(Intercept) 11.11 11.36
g6pd_202_rtpcrHET -0.3351 0.2571
g6pd_202_rtpcrHOM/HEMI -0.1426 0.4665
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.19 0.13 78.37 1.033e-272 * * *
g6pd_202_rtpcrHET -0.005396 0.1393 -0.03873 0.9691
g6pd_202_rtpcrHOM/HEMI 0.0912 0.1435 0.6357 0.5253
age_at_collection_years_2010 0.1382 0.01529 9.041 4.029e-18 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.089 0.1499 0.1445
  2.5 % 97.5 %
(Intercept) 9.933 10.44
g6pd_202_rtpcrHET -0.2791 0.2683
g6pd_202_rtpcrHOM/HEMI -0.1907 0.3731
age_at_collection_years_2010 0.1082 0.1683

g6pd_202_rtpcr _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.3 0.1685 61.1 6.279e-181 * * *
thalHET -0.2438 0.1374 -1.774 0.07691
thalHOM -0.6807 0.1612 -4.223 3.13e-05 * * *
age_at_collection_years_2010 0.1587 0.01821 8.718 1.454e-16 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.085 0.2165 0.2094
  2.5 % 97.5 %
(Intercept) 9.966 10.63
thalHET -0.5141 0.02648
thalHOM -0.9978 -0.3636
age_at_collection_years_2010 0.1229 0.1946
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.49600 0.1030997 327 11.29318 11.69882
  • HET 11.25221 0.0907669 327 11.07365 11.43077
  • HOM 10.81530 0.1237658 327 10.57183 11.05878
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.46 0.1142 100.4 2.866e-248 * * *
thalHET -0.2009 0.1522 -1.32 0.1877
thalHOM -0.6071 0.1784 -3.403 0.0007502 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.203 0.03446 0.02857
  2.5 % 97.5 %
(Intercept) 11.24 11.68
thalHET -0.5003 0.09847
thalHOM -0.9581 -0.2561
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.46036 0.1141906 328 11.23572 11.68500
  • HET 11.25944 0.1006060 328 11.06153 11.45735
  • HOM 10.85325 0.1371029 328 10.58353 11.12296
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.06 0.1548 64.97 2.302e-189 * * *
sickleHET 0.05957 0.1682 0.354 0.7235
age_at_collection_years_2010 0.1545 0.01866 8.279 3.188e-15 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.113 0.1739 0.1689
  2.5 % 97.5 %
(Intercept) 9.752 10.36
sickleHET -0.2714 0.3906
age_at_collection_years_2010 0.1178 0.1912
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.22297 0.0666317 328 11.09189 11.35405
  • HET 11.28253 0.1544453 328 10.97871 11.58636
sickle n
NORM 279
HET 52

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.21 0.07314 153.3 4.762e-308 * * *
sickleHET 0.1214 0.1845 0.6576 0.5112
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.222 0.001313 -0.001723
  2.5 % 97.5 %
(Intercept) 11.07 11.36
sickleHET -0.2417 0.4844
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.21326 0.0731405 329 11.06938 11.35714
  • HET 11.33462 0.1694175 329 11.00134 11.66789
sickle n
NORM 279
HET 52

g6pd_202_rtpcr _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.06 0.3494 31.64 1.357e-43 * * *
thalHET -0.5877 0.2859 -2.056 0.04348 *
thalHOM -0.6736 0.3983 -1.691 0.09519
age_at_collection_years_2010 0.07623 0.03951 1.93 0.05764
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 1.091 0.09776 0.05964
  2.5 % 97.5 %
(Intercept) 10.36 11.75
thalHET -1.158 -0.01768
thalHOM -1.468 0.1206
age_at_collection_years_2010 -0.002537 0.155
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.61456 0.2285846 71 11.15878 12.07035
  • HET 11.02683 0.1725376 71 10.68280 11.37086
  • HOM 10.94097 0.3214778 71 10.29997 11.58198
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.57 0.2317 49.94 1.278e-57 * * *
thalHET -0.5546 0.2907 -1.908 0.06044
thalHOM -0.5029 0.3956 -1.271 0.2078
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 1.111 0.05044 0.02406
  2.5 % 97.5 %
(Intercept) 11.11 12.03
thalHET -1.134 0.02498
thalHOM -1.292 0.2858
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.56957 0.2316533 72 11.10777 12.03136
  • HET 11.01500 0.1756598 72 10.66483 11.36517
  • HOM 11.06667 0.3207095 72 10.42734 11.70599
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.91 0.3278 33.29 1.798e-45 * * *
sickleHET -0.5788 0.3292 -1.758 0.08299
age_at_collection_years_2010 0.05312 0.03919 1.356 0.1795
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 1.096 0.07509 0.04939
  2.5 % 97.5 %
(Intercept) 10.26 11.57
sickleHET -1.235 0.07749
age_at_collection_years_2010 -0.025 0.1312
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.30137 0.1407335 72 11.02082 11.58192
  • HET 10.72260 0.2961807 72 10.13217 11.31303
sickle n
NORM 61
HET 14

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.31 0.1412 80.14 6.651e-73 * * *
sickleHET -0.6505 0.3268 -1.99 0.05028
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 1.103 0.05148 0.03849
  2.5 % 97.5 %
(Intercept) 11.03 11.6
sickleHET -1.302 0.0008258
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.31475 0.1411900 73 11.03336 11.59615
  • HET 10.66429 0.2947168 73 10.07692 11.25166
sickle n
NORM 61
HET 14

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.56 0.3153 33.49 3.867e-43 * * *
thalHET -0.2369 0.2387 -0.9921 0.3248
thalHOM -0.9555 0.3365 -2.84 0.005993 * *
age_at_collection_years_2010 0.1366 0.03362 4.062 0.0001316 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.8822 0.28 0.2473
  2.5 % 97.5 %
(Intercept) 9.929 11.19
thalHET -0.7135 0.2398
thalHOM -1.627 -0.2838
age_at_collection_years_2010 0.06945 0.2037
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.65937 0.1889378 66 11.28214 12.03659
  • HET 11.42251 0.1439818 66 11.13505 11.70998
  • HOM 10.70384 0.2797318 66 10.14534 11.26234
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.59 0.2087 55.52 9.483e-58 * * *
thalHET -0.09952 0.2623 -0.3795 0.7055
thalHOM -0.9664 0.3733 -2.588 0.01181 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.9789 0.09998 0.07311
  2.5 % 97.5 %
(Intercept) 11.17 12
thalHET -0.623 0.4239
thalHOM -1.712 -0.2212
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.58636 0.2087069 67 11.16978 12.00294
  • HET 11.48684 0.1588021 67 11.16987 11.80381
  • HOM 10.62000 0.3095624 67 10.00211 11.23789
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.28 0.3076 33.44 1.593e-43 * * *
sickleHET -0.04989 0.2943 -0.1695 0.8659
age_at_collection_years_2010 0.1388 0.0349 3.975 0.0001747 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.9278 0.1915 0.1673
  2.5 % 97.5 %
(Intercept) 9.67 10.9
sickleHET -0.6373 0.5375
age_at_collection_years_2010 0.06909 0.2084
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.40284 0.1218348 67 11.15966 11.64602
  • HET 11.35295 0.2678880 67 10.81824 11.88765
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.41 0.1344 84.85 1.012e-70 * * *
sickleHET -0.07356 0.3247 -0.2266 0.8214
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 1.024 0.0007542 -0.01394
  2.5 % 97.5 %
(Intercept) 11.14 11.68
sickleHET -0.7215 0.5744
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.40690 0.1344396 68 11.13863 11.67517
  • HET 11.33333 0.2955634 68 10.74355 11.92312
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.49 0.09267 124 0 * * *
thalHET -0.2399 0.121 -1.982 0.04803 *
thalHOM -0.6387 0.1487 -4.294 2.129e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.157 0.03753 0.03346
  2.5 % 97.5 %
(Intercept) 11.31 11.68
thalHET -0.4778 -0.002093
thalHOM -0.9309 -0.3464
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.45 0.1372 76.14 3.031e-267 * * *
thalHET -0.2994 0.1108 -2.703 0.007116 * *
thalHOM -0.7299 0.1362 -5.358 1.321e-07 * * *
age_at_collection_years_2010 0.1444 0.01486 9.718 1.806e-20 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.058 0.198 0.1929
  2.5 % 97.5 %
(Intercept) 10.18 10.72
thalHET -0.5171 -0.08177
thalHOM -0.9976 -0.4622
age_at_collection_years_2010 0.1152 0.1736

thal _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.33 0.2241 46.09 3.145e-91 * * *
g6pd_202_rtpcrHET 0.2031 0.2543 0.7986 0.4258
g6pd_202_rtpcrHOM/HEMI 0.09486 0.2586 0.3669 0.7142
age_at_collection_years_2010 0.1542 0.02702 5.707 5.841e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 1.108 0.1781 0.1619
  2.5 % 97.5 %
(Intercept) 9.888 10.77
g6pd_202_rtpcrHET -0.2994 0.7056
g6pd_202_rtpcrHOM/HEMI -0.416 0.6057
age_at_collection_years_2010 0.1008 0.2076
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.45091 0.1051588 152 11.24315 11.65867
  • HET 11.65401 0.2314615 152 11.19671 12.11131
  • HOM/HEMI 11.54577 0.2362865 152 11.07894 12.01260
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.46 0.1155 99.24 8.673e-141 * * *
g6pd_202_rtpcrHET 0.1092 0.2788 0.3918 0.6958
g6pd_202_rtpcrHOM/HEMI 0.126 0.2839 0.4438 0.6578
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 1.217 0.001959 -0.01109
  2.5 % 97.5 %
(Intercept) 11.23 11.69
g6pd_202_rtpcrHET -0.4415 0.6599
g6pd_202_rtpcrHOM/HEMI -0.435 0.687
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.46036 0.1154854 153 11.23221 11.68851
  • HET 11.56957 0.2537024 153 11.06835 12.07078
  • HOM/HEMI 11.58636 0.2594043 153 11.07389 12.09884
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.4 0.2192 47.44 2.087e-93 * * *
sickleHET -0.08782 0.2377 -0.3694 0.7123
age_at_collection_years_2010 0.1528 0.02692 5.675 6.761e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 1.106 0.1751 0.1643
  2.5 % 97.5 %
(Intercept) 9.966 10.83
sickleHET -0.5575 0.3818
age_at_collection_years_2010 0.09959 0.206
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.50887 0.0970235 153 11.31719 11.70055
  • HET 11.42104 0.2169984 153 10.99234 11.84974
sickle n
NORM 130
HET 26

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.51 0.1064 108.2 3.353e-147 * * *
sickleHET -0.1223 0.2606 -0.4693 0.6395
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 1.213 0.001428 -0.005056
  2.5 % 97.5 %
(Intercept) 11.3 11.72
sickleHET -0.6371 0.3925
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.51462 0.1063941 154 11.30443 11.72480
  • HET 11.39231 0.2379045 154 10.92233 11.86229
sickle n
NORM 130
HET 26

thal _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.16 0.1879 54.06 7.12e-128 * * *
g6pd_202_rtpcrHET -0.1801 0.1924 -0.9364 0.3501
g6pd_202_rtpcrHOM/HEMI 0.09155 0.1971 0.4645 0.6427
age_at_collection_years_2010 0.1449 0.02173 6.668 2.116e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 1.074 0.1819 0.1705
  2.5 % 97.5 %
(Intercept) 9.788 10.53
g6pd_202_rtpcrHET -0.5592 0.199
g6pd_202_rtpcrHOM/HEMI -0.2969 0.48
age_at_collection_years_2010 0.1021 0.1878
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.27116 0.0898336 217 11.09410 11.44822
  • HET 11.09104 0.1702044 217 10.75558 11.42651
  • HOM/HEMI 11.36270 0.1752254 217 11.01734 11.70807
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.26 0.09836 114.5 1.131e-196 * * *
g6pd_202_rtpcrHET -0.2444 0.2104 -1.162 0.2466
g6pd_202_rtpcrHOM/HEMI 0.2274 0.2147 1.059 0.2906
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 1.176 0.01421 0.005171
  2.5 % 97.5 %
(Intercept) 11.07 11.45
g6pd_202_rtpcrHET -0.6591 0.1702
g6pd_202_rtpcrHOM/HEMI -0.1957 0.6505
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.25944 0.0983633 218 11.06558 11.45331
  • HET 11.01500 0.1859821 218 10.64845 11.38155
  • HOM/HEMI 11.48684 0.1908136 218 11.11077 11.86292
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.13 0.1816 55.81 4.425e-131 * * *
sickleHET -0.1304 0.1984 -0.6575 0.5115
age_at_collection_years_2010 0.1487 0.02161 6.88 6.255e-11 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 1.074 0.1785 0.1709
  2.5 % 97.5 %
(Intercept) 9.776 10.49
sickleHET -0.5214 0.2605
age_at_collection_years_2010 0.1061 0.1913
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.27496 0.0787682 218 11.11971 11.43020
  • HET 11.14452 0.1819157 218 10.78598 11.50305
sickle n
NORM 186
HET 35

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.26 0.08666 129.9 3.213e-209 * * *
sickleHET -0.03057 0.2178 -0.1404 0.8885
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 1.182 8.996e-05 -0.004476
  2.5 % 97.5 %
(Intercept) 11.09 11.43
sickleHET -0.4598 0.3986
  • sickle emmean SE df lower.CL upper.CL
  • NORM 11.25914 0.0866643 219 11.08834 11.42994
  • HET 11.22857 0.1997849 219 10.83482 11.62232
sickle n
NORM 186
HET 35

thal _ HOM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.942 0.2749 36.17 2.378e-57 * * *
g6pd_202_rtpcrHET 0.07696 0.3005 0.2561 0.7984
g6pd_202_rtpcrHOM/HEMI -0.1934 0.3231 -0.5986 0.5509
age_at_collection_years_2010 0.117 0.03237 3.614 0.0004852 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.9605 0.1304 0.1029
  2.5 % 97.5 %
(Intercept) 9.397 10.49
g6pd_202_rtpcrHET -0.5196 0.6735
g6pd_202_rtpcrHOM/HEMI -0.8347 0.448
age_at_collection_years_2010 0.05271 0.1812
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 10.86576 0.1095163 95 10.64834 11.08318
  • HET 10.94272 0.2793921 95 10.38805 11.49738
  • HOM/HEMI 10.67239 0.3040892 95 10.06870 11.27608
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.85 0.1161 93.46 4.519e-96 * * *
g6pd_202_rtpcrHET 0.2134 0.3163 0.6748 0.5014
g6pd_202_rtpcrHOM/HEMI -0.2332 0.3425 -0.6809 0.4976
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 1.019 0.01082 -0.009792
  2.5 % 97.5 %
(Intercept) 10.62 11.08
g6pd_202_rtpcrHET -0.4144 0.8412
g6pd_202_rtpcrHOM/HEMI -0.9132 0.4467
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 10.85325 0.1161326 96 10.622725 11.08377
  • HET 11.06667 0.2941772 96 10.482729 11.65060
  • HOM/HEMI 10.62000 0.3222550 96 9.980329 11.25967
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.858 0.2802 35.18 1.196e-56 * * *
sickleHET 0.1942 0.2567 0.7568 0.451
age_at_collection_years_2010 0.1222 0.03216 3.799 0.0002547 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.955 0.1313 0.1132
  2.5 % 97.5 %
(Intercept) 9.301 10.41
sickleHET -0.3152 0.7037
age_at_collection_years_2010 0.05834 0.186
  • sickle emmean SE df lower.CL upper.CL
  • NORM 10.82220 0.1056174 96 10.61255 11.03185
  • HET 11.01644 0.2332561 96 10.55343 11.47945
sickle n
NORM 82
HET 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.84 0.1125 96.37 4.063e-98 * * *
sickleHET 0.06786 0.2716 0.2499 0.8032
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 1.019 0.0006434 -0.009659
  2.5 % 97.5 %
(Intercept) 10.62 11.07
sickleHET -0.4711 0.6068
  • sickle emmean SE df lower.CL upper.CL
  • NORM 10.84390 0.1125289 97 10.62056 11.06724
  • HET 10.91176 0.2471421 97 10.42126 11.40227
sickle n
NORM 82
HET 17

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.26 0.05907 190.6 0 * * *
sickleHET -0.04293 0.1459 -0.2942 0.7687
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.178 0.0001826 -0.001927
  2.5 % 97.5 %
(Intercept) 11.14 11.37
sickleHET -0.3297 0.2438
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.2 0.1279 79.81 1.452e-276 * * *
sickleHET -0.04362 0.1347 -0.3237 0.7463
age_at_collection_years_2010 0.1388 0.01524 9.108 2.374e-18 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.088 0.1494 0.1458
  2.5 % 97.5 %
(Intercept) 9.953 10.46
sickleHET -0.3084 0.2212
age_at_collection_years_2010 0.1089 0.1688

sickle _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.17 0.1405 72.42 8.755e-230 * * *
g6pd_202_rtpcrHET 0.09155 0.1539 0.5949 0.5523
g6pd_202_rtpcrHOM/HEMI 0.1098 0.1574 0.6977 0.4858
age_at_collection_years_2010 0.1387 0.01662 8.345 1.212e-15 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 1.089 0.1534 0.1469
  2.5 % 97.5 %
(Intercept) 9.898 10.45
g6pd_202_rtpcrHET -0.211 0.3941
g6pd_202_rtpcrHOM/HEMI -0.1997 0.4194
age_at_collection_years_2010 0.106 0.1714
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.22700 0.0652048 394 11.09880 11.35519
  • HET 11.31854 0.1394058 394 11.04447 11.59262
  • HOM/HEMI 11.33684 0.1432112 394 11.05529 11.61839
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.21 0.07062 158.8 0 * * *
g6pd_202_rtpcrHET 0.1015 0.1667 0.6087 0.5431
g6pd_202_rtpcrHOM/HEMI 0.1936 0.1702 1.137 0.256
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 1.18 0.003699 -0.001346
  2.5 % 97.5 %
(Intercept) 11.07 11.35
g6pd_202_rtpcrHET -0.2263 0.4293
g6pd_202_rtpcrHOM/HEMI -0.141 0.5283
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.21326 0.0706214 395 11.07442 11.35210
  • HET 11.31475 0.1510336 395 11.01782 11.61168
  • HOM/HEMI 11.40690 0.1548904 395 11.10238 11.71141
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.44 0.1496 69.84 5.118e-224 * * *
thalHET -0.2948 0.1205 -2.446 0.01488 *
thalHOM -0.7838 0.1491 -5.257 2.4e-07 * * *
age_at_collection_years_2010 0.1465 0.01611 9.096 4.694e-18 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 1.053 0.2074 0.2014
  2.5 % 97.5 %
(Intercept) 10.15 10.74
thalHET -0.5317 -0.05785
thalHOM -1.077 -0.4907
age_at_collection_years_2010 0.1148 0.1782
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.55627 0.0925067 394 11.37440 11.73814
  • HET 11.26151 0.0772428 394 11.10965 11.41337
  • HOM 10.77249 0.1165982 394 10.54326 11.00173
thal n
NORM 130
HET 186
HOM 82

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.51 0.1015 113.4 1.624e-303 * * *
thalHET -0.2555 0.1323 -1.931 0.0542
thalHOM -0.6707 0.1632 -4.11 4.823e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 1.157 0.04101 0.03615
  2.5 % 97.5 %
(Intercept) 11.32 11.71
thalHET -0.5156 0.004629
thalHOM -0.9916 -0.3498
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.51462 0.1015032 395 11.31506 11.71417
  • HET 11.25914 0.0848584 395 11.09231 11.42597
  • HOM 10.84390 0.1278040 395 10.59264 11.09516
thal n
NORM 130
HET 186
HOM 82

sickle _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.35 0.3512 29.49 1.203e-42 * * *
g6pd_202_rtpcrHET -0.4618 0.3384 -1.365 0.1765
g6pd_202_rtpcrHOM/HEMI -0.005445 0.3528 -0.01543 0.9877
age_at_collection_years_2010 0.1243 0.04009 3.1 0.002739 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 1.102 0.1577 0.1236
  2.5 % 97.5 %
(Intercept) 9.655 11.05
g6pd_202_rtpcrHET -1.136 0.2125
g6pd_202_rtpcrHOM/HEMI -0.7084 0.6975
age_at_collection_years_2010 0.04439 0.2042
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.29783 0.1532218 74 10.99253 11.60313
  • HET 10.83602 0.2995770 74 10.23910 11.43294
  • HOM/HEMI 11.29238 0.3182728 74 10.65821 11.92656
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.33 0.1613 70.27 3.354e-70 * * *
g6pd_202_rtpcrHET -0.6703 0.3502 -1.914 0.05942
g6pd_202_rtpcrHOM/HEMI -0.001282 0.3725 -0.003442 0.9973
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 1.163 0.04838 0.023
  2.5 % 97.5 %
(Intercept) 11.01 11.66
g6pd_202_rtpcrHET -1.368 0.0273
g6pd_202_rtpcrHOM/HEMI -0.7433 0.7407
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 11.33462 0.1612901 75 11.01331 11.65592
  • HET 10.66429 0.3108460 75 10.04505 11.28352
  • HOM/HEMI 11.33333 0.3357521 75 10.66448 12.00219
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.41 0.3551 29.31 1.816e-42 * * *
thalHET -0.3262 0.2884 -1.131 0.2618
thalHOM -0.4754 0.3429 -1.386 0.1698
age_at_collection_years_2010 0.1394 0.03987 3.496 0.0008027 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 1.099 0.1609 0.1269
  2.5 % 97.5 %
(Intercept) 9.699 11.11
thalHET -0.9009 0.2485
thalHOM -1.159 0.2079
age_at_collection_years_2010 0.05993 0.2188
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.46407 0.2166006 74 11.03249 11.89566
  • HET 11.13790 0.1876469 74 10.76401 11.51180
  • HOM 10.98867 0.2675688 74 10.45553 11.52181
thal n
NORM 26
HET 35
HOM 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.39 0.2312 49.28 6.933e-59 * * *
thalHET -0.1637 0.3052 -0.5365 0.5932
thalHOM -0.4805 0.3677 -1.307 0.1952
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 1.179 0.02239 -0.003681
  2.5 % 97.5 %
(Intercept) 10.93 11.85
thalHET -0.7718 0.4443
thalHOM -1.213 0.2519
  • thal emmean SE df lower.CL upper.CL
  • NORM 11.39231 0.2311926 75 10.93175 11.85287
  • HET 11.22857 0.1992630 75 10.83162 11.62552
  • HOM 10.91176 0.2859145 75 10.34219 11.48134
thal n
NORM 26
HET 35
HOM 17

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

mcv_2010

####All_vs_g6pd+thal________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.81 0.5459 140.7 0 * * *
g6pd_202_rtpcrHET 2.637 0.8228 3.205 0.001443 * *
g6pd_202_rtpcrHOM/HEMI 5.87 0.8472 6.929 1.396e-11 * * *
thalHET -4.326 0.6719 -6.438 2.982e-10 * * *
thalHOM -12.09 0.8252 -14.65 2.541e-40 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 6.413 0.3726 0.3673
  2.5 % 97.5 %
(Intercept) 75.74 77.89
g6pd_202_rtpcrHET 1.02 4.254
g6pd_202_rtpcrHOM/HEMI 4.206 7.535
thalHET -5.646 -3.006
thalHOM -13.71 -10.47
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 76.81411 0.5459196 471 75.74137 77.88685
  • HET NORM 79.45121 0.8596346 471 77.76202 81.14041
  • HOM/HEMI NORM 82.68437 0.8804315 471 80.95431 84.41443
  • NORM HET 72.48799 0.4874002 471 71.53024 73.44574
  • HET HET 75.12510 0.7906102 471 73.57154 76.67866
  • HOM/HEMI HET 78.35825 0.8134077 471 76.75990 79.95661
  • NORM HOM 64.72275 0.6602289 471 63.42539 66.02011
  • HET HOM 67.35985 0.9605738 471 65.47231 69.24739
  • HOM/HEMI HOM 70.59301 0.9885599 471 68.65048 72.53555
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.44 0.7877 90.69 6.318e-300 * * *
g6pd_202_rtpcrHET 2.814 0.7622 3.691 0.0002495 * * *
g6pd_202_rtpcrHOM/HEMI 5.479 0.7857 6.974 1.054e-11 * * *
thalHET -4.627 0.6232 -7.425 5.359e-13 * * *
thalHOM -12.57 0.7661 -16.41 3.569e-48 * * *
age_at_collection_years_2010 0.7442 0.08361 8.9 1.213e-17 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 5.939 0.4631 0.4574
  2.5 % 97.5 %
(Intercept) 69.89 72.99
g6pd_202_rtpcrHET 1.316 4.311
g6pd_202_rtpcrHOM/HEMI 3.935 7.023
thalHET -5.851 -3.402
thalHOM -14.08 -11.07
age_at_collection_years_2010 0.5799 0.9085
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 77.08343 0.5064576 470 76.08823 78.07863
  • HET NORM 79.89694 0.7976446 470 78.32955 81.46433
  • HOM/HEMI NORM 82.56282 0.8154445 470 80.96045 84.16519
  • NORM HET 72.45673 0.4513742 470 71.56977 73.34369
  • HET HET 75.27024 0.7323320 470 73.83119 76.70929
  • HOM/HEMI HET 77.93612 0.7547539 470 76.45301 79.41923
  • NORM HOM 64.51088 0.6118731 470 63.30854 65.71323
  • HET HOM 67.32439 0.8895555 470 65.57639 69.07239
  • HOM/HEMI HOM 69.99027 0.9179645 470 68.18645 71.79409
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

####All_vs_g6pd+sickle________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.26 0.4505 160.4 0 * * *
g6pd_202_rtpcrHET 3.1 0.9884 3.137 0.001815 * *
g6pd_202_rtpcrHOM/HEMI 6.488 1.016 6.382 4.172e-10 * * *
sickleHET -0.7936 0.9571 -0.8292 0.4074
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 7.726 0.08757 0.08177
  2.5 % 97.5 %
(Intercept) 71.37 73.14
g6pd_202_rtpcrHET 1.158 5.043
g6pd_202_rtpcrHOM/HEMI 4.49 8.485
sickleHET -2.674 1.087
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 72.25701 0.4504931 472 71.37179 73.14223
  • HET NORM 75.35748 0.9098348 472 73.56965 77.14531
  • HOM/HEMI NORM 78.74463 0.9378962 472 76.90166 80.58759
  • NORM HET 71.46336 0.9116910 472 69.67188 73.25483
  • HET HET 74.56383 1.1840040 472 72.23726 76.89040
  • HOM/HEMI HET 77.95098 1.2172237 472 75.55913 80.34282
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.36 0.8994 74.9 9.204e-264 * * *
g6pd_202_rtpcrHET 3.258 0.9517 3.424 0.0006717 * * *
g6pd_202_rtpcrHOM/HEMI 6.156 0.9798 6.283 7.57e-10 * * *
sickleHET -0.7983 0.9212 -0.8666 0.3866
age_at_collection_years_2010 0.6483 0.1044 6.21 1.168e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 7.436 0.1566 0.1494
  2.5 % 97.5 %
(Intercept) 65.6 69.13
g6pd_202_rtpcrHET 1.388 5.128
g6pd_202_rtpcrHOM/HEMI 4.231 8.081
sickleHET -2.608 1.012
age_at_collection_years_2010 0.4431 0.8534
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 72.28171 0.4335908 471 71.42970 73.13372
  • HET NORM 75.53989 0.8761541 471 73.81824 77.26155
  • HOM/HEMI NORM 78.43753 0.9040226 471 76.66111 80.21394
  • NORM HET 71.48343 0.8774539 471 69.75922 73.20764
  • HET HET 74.74161 1.1398925 471 72.50171 76.98152
  • HOM/HEMI HET 77.63925 1.1725800 471 75.33511 79.94338
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.13 0.4245 169.9 0 * * *
g6pd_202_rtpcrHET 3.077 0.9877 3.115 0.001949 * *
g6pd_202_rtpcrHOM/HEMI 6.476 1.016 6.374 4.383e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 7.723 0.08624 0.08237
  2.5 % 97.5 %
(Intercept) 71.3 72.97
g6pd_202_rtpcrHET 1.136 5.018
g6pd_202_rtpcrHOM/HEMI 4.48 8.473
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.24 0.8876 75.75 2.817e-266 * * *
g6pd_202_rtpcrHET 3.235 0.951 3.401 0.000728 * * *
g6pd_202_rtpcrHOM/HEMI 6.144 0.9794 6.274 7.992e-10 * * *
age_at_collection_years_2010 0.6482 0.1044 6.211 1.159e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 7.434 0.1553 0.1499
  2.5 % 97.5 %
(Intercept) 65.49 68.98
g6pd_202_rtpcrHET 1.366 5.103
g6pd_202_rtpcrHOM/HEMI 4.22 8.069
age_at_collection_years_2010 0.4431 0.8533

g6pd_202_rtpcr _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.13 0.9286 76.6 6.114e-211 * * *
thalHET -5.257 0.757 -6.944 2.056e-11 * * *
thalHOM -12.57 0.8881 -14.16 8.034e-36 * * *
age_at_collection_years_2010 0.8206 0.1003 8.178 6.432e-15 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 5.98 0.439 0.4339
  2.5 % 97.5 %
(Intercept) 69.31 72.96
thalHET -6.746 -3.768
thalHOM -14.32 -10.83
age_at_collection_years_2010 0.6232 1.018
  • thal emmean SE df lower.CL upper.CL
  • NORM 77.32835 0.5680443 327 76.21087 78.44584
  • HET 72.07171 0.5000946 327 71.08790 73.05552
  • HOM 64.75452 0.6819074 327 63.41304 66.09600
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.14 0.622 124 1.192e-277 * * *
thalHET -5.035 0.829 -6.074 3.457e-09 * * *
thalHOM -12.19 0.9719 -12.55 9.294e-30 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 6.553 0.3243 0.3202
  2.5 % 97.5 %
(Intercept) 75.92 78.37
thalHET -6.666 -3.404
thalHOM -14.11 -10.28
  • thal emmean SE df lower.CL upper.CL
  • NORM 77.14414 0.6219989 328 75.92053 78.36775
  • HET 72.10909 0.5480032 328 71.03105 73.18714
  • HOM 64.95065 0.7468025 328 63.48152 66.41978
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 66.53 1.055 63.07 1.872e-185 * * *
sickleHET -0.2253 1.147 -0.1966 0.8443
age_at_collection_years_2010 0.7471 0.1272 5.875 1.038e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 7.583 0.09522 0.0897
  2.5 % 97.5 %
(Intercept) 64.45 68.6
sickleHET -2.481 2.03
age_at_collection_years_2010 0.4969 0.9973
  • sickle emmean SE df lower.CL upper.CL
  • NORM 72.16773 0.4540489 328 71.27451 73.06094
  • HET 71.94238 1.0524377 328 69.87200 74.01276
sickle n
NORM 279
HET 52

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.12 0.4765 151.3 3.156e-306 * * *
sickleHET 0.07344 1.202 0.06108 0.9513
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 7.96 1.134e-05 -0.003028
  2.5 % 97.5 %
(Intercept) 71.18 73.06
sickleHET -2.292 2.439
  • sickle emmean SE df lower.CL upper.CL
  • NORM 72.12079 0.4765416 329 71.18334 73.05824
  • HET 72.19423 1.1038275 329 70.02278 74.36568
sickle n
NORM 279
HET 52

g6pd_202_rtpcr _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.96 1.843 40.68 6.173e-51 * * *
thalHET -4.123 1.508 -2.735 0.007878 * *
thalHOM -12.32 2.1 -5.866 1.301e-07 * * *
age_at_collection_years_2010 0.6055 0.2083 2.907 0.00487 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 5.75 0.3433 0.3156
  2.5 % 97.5 %
(Intercept) 71.28 78.63
thalHET -7.129 -1.117
thalHOM -16.51 -8.131
age_at_collection_years_2010 0.1901 1.021
  • thal emmean SE df lower.CL upper.CL
  • NORM 79.37914 1.2053305 71 76.97578 81.78250
  • HET 75.25649 0.9097938 71 73.44241 77.07057
  • HOM 67.06002 1.6951580 71 63.67997 70.44007
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 79.02 1.26 62.74 1.36e-64 * * *
thalHET -3.859 1.581 -2.441 0.01709 *
thalHOM -10.96 2.151 -5.097 2.683e-06 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 6.041 0.2652 0.2448
  2.5 % 97.5 %
(Intercept) 76.51 81.53
thalHET -7.01 -0.7082
thalHOM -15.25 -6.675
  • thal emmean SE df lower.CL upper.CL
  • NORM 79.02174 1.2595390 72 76.51089 81.53258
  • HET 75.16250 0.9550927 72 73.25856 77.06644
  • HOM 68.05833 1.7437527 72 64.58223 71.53444
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.96 2.079 35.1 5.023e-47 * * *
sickleHET -0.5857 2.088 -0.2805 0.7799
age_at_collection_years_2010 0.3229 0.2485 1.299 0.1981
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 6.954 0.02615 -0.0009045
  2.5 % 97.5 %
(Intercept) 68.82 77.1
sickleHET -4.748 3.576
age_at_collection_years_2010 -0.1726 0.8183
  • sickle emmean SE df lower.CL upper.CL
  • NORM 75.31866 0.8925545 72 73.53939 77.09794
  • HET 74.73297 1.8784252 72 70.98840 78.47754
sickle n
NORM 61
HET 14

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.4 0.8945 84.29 1.733e-74 * * *
sickleHET -1.021 2.07 -0.4933 0.6233
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 6.987 0.003323 -0.01033
  2.5 % 97.5 %
(Intercept) 73.62 77.18
sickleHET -5.148 3.105
  • sickle emmean SE df lower.CL upper.CL
  • NORM 75.40000 0.8945381 73 73.61719 77.18281
  • HET 74.37857 1.8672383 73 70.65717 78.09997
sickle n
NORM 61
HET 14

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.07 2.11 37 7.416e-46 * * *
thalHET -2.177 1.598 -1.362 0.1777
thalHOM -13.43 2.252 -5.965 1.063e-07 * * *
age_at_collection_years_2010 0.4508 0.225 2.003 0.04923 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 5.904 0.3938 0.3662
  2.5 % 97.5 %
(Intercept) 73.86 82.29
thalHET -5.367 1.013
thalHOM -17.93 -8.937
age_at_collection_years_2010 0.001556 0.9001
  • thal emmean SE df lower.CL upper.CL
  • NORM 81.70915 1.2644524 66 79.18459 84.23371
  • HET 79.53241 0.9635876 66 77.60854 81.45627
  • HOM 68.27673 1.8720847 66 64.53899 72.01447
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 81.47 1.287 63.31 1.669e-61 * * *
thalHET -1.723 1.617 -1.066 0.2903
thalHOM -13.47 2.302 -5.851 1.606e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 6.035 0.3569 0.3377
  2.5 % 97.5 %
(Intercept) 78.9 84.04
thalHET -4.951 1.504
thalHOM -18.06 -8.874
  • thal emmean SE df lower.CL upper.CL
  • NORM 81.46818 1.2867189 67 78.89988 84.03648
  • HET 79.74474 0.9790463 67 77.79055 81.69892
  • HOM 68.00000 1.9085126 67 64.19059 71.80941
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 75.29 2.371 31.75 4.143e-42 * * *
sickleHET -4.27 2.269 -1.882 0.06422
age_at_collection_years_2010 0.5021 0.2691 1.866 0.06643
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 7.153 0.09664 0.06968
  2.5 % 97.5 %
(Intercept) 70.56 80.03
sickleHET -8.798 0.2593
age_at_collection_years_2010 -0.035 1.039
  • sickle emmean SE df lower.CL upper.CL
  • NORM 79.34049 0.9392651 67 77.46571 81.21527
  • HET 75.07097 2.0652373 67 70.94873 79.19320
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 79.36 0.9562 82.99 4.499e-70 * * *
sickleHET -4.355 2.309 -1.886 0.0636
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 7.282 0.0497 0.03572
  2.5 % 97.5 %
(Intercept) 77.45 81.26
sickleHET -8.964 0.2533
  • sickle emmean SE df lower.CL upper.CL
  • NORM 79.35517 0.9562174 68 77.44707 81.26327
  • HET 75.00000 2.1022290 68 70.80507 79.19493
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.03 0.5399 144.5 0 * * *
thalHET -4.056 0.7051 -5.753 1.58e-08 * * *
thalHOM -12.4 0.8664 -14.31 7.762e-39 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 6.743 0.3036 0.3006
  2.5 % 97.5 %
(Intercept) 76.97 79.09
thalHET -5.442 -2.671
thalHOM -14.1 -10.69
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.44 0.8121 89.2 1.386e-297 * * *
thalHET -4.373 0.6558 -6.669 7.237e-11 * * *
thalHOM -12.88 0.8065 -15.97 3.196e-46 * * *
age_at_collection_years_2010 0.7692 0.08796 8.746 3.928e-17 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 6.262 0.4007 0.3969
  2.5 % 97.5 %
(Intercept) 70.85 74.04
thalHET -5.662 -3.085
thalHOM -14.47 -11.3
age_at_collection_years_2010 0.5964 0.9421

thal _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.91 1.468 47.62 3.051e-93 * * *
g6pd_202_rtpcrHET 2.479 1.666 1.488 0.1388
g6pd_202_rtpcrHOM/HEMI 4.125 1.694 2.435 0.01604 *
age_at_collection_years_2010 0.9879 0.177 5.581 1.07e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 7.256 0.2007 0.1849
  2.5 % 97.5 %
(Intercept) 67.01 72.81
g6pd_202_rtpcrHET -0.8121 5.77
g6pd_202_rtpcrHOM/HEMI 0.7783 7.471
age_at_collection_years_2010 0.6382 1.338
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 77.08360 0.6887921 152 75.72276 78.44444
  • HET 79.56265 1.5160763 152 76.56735 82.55795
  • HOM/HEMI 81.20817 1.5476803 152 78.15043 84.26591
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.14 0.7535 102.4 7.884e-143 * * *
g6pd_202_rtpcrHET 1.878 1.819 1.032 0.3035
g6pd_202_rtpcrHOM/HEMI 4.324 1.853 2.334 0.0209 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 7.939 0.03693 0.02434
  2.5 % 97.5 %
(Intercept) 75.66 78.63
g6pd_202_rtpcrHET -1.716 5.471
g6pd_202_rtpcrHOM/HEMI 0.6639 7.984
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 77.14414 0.7535107 153 75.65552 78.63277
  • HET 79.02174 1.6553392 153 75.75147 82.29201
  • HOM/HEMI 81.46818 1.6925425 153 78.12441 84.81195
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.05 1.465 48.5 8.8e-95 * * *
sickleHET -0.8678 1.589 -0.5462 0.5857
age_at_collection_years_2010 0.981 0.1799 5.452 1.954e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 7.393 0.1648 0.1539
  2.5 % 97.5 %
(Intercept) 68.16 73.94
sickleHET -4.007 2.271
age_at_collection_years_2010 0.6256 1.336
  • sickle emmean SE df lower.CL upper.CL
  • NORM 78.1754 0.6484273 153 76.89438 79.45643
  • HET 77.3076 1.4502436 153 74.44251 80.17269
sickle n
NORM 130
HET 26

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.21 0.7063 110.7 1.024e-148 * * *
sickleHET -1.089 1.73 -0.6296 0.5299
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 8.053 0.002567 -0.003909
  2.5 % 97.5 %
(Intercept) 76.82 79.61
sickleHET -4.507 2.328
  • sickle emmean SE df lower.CL upper.CL
  • NORM 78.21231 0.7062822 154 76.81706 79.60756
  • HET 77.12308 1.5792949 154 74.00320 80.24296
sickle n
NORM 130
HET 26

thal _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 66.7 0.9227 72.29 8.216e-154 * * *
g6pd_202_rtpcrHET 3.37 0.9444 3.568 0.0004426 * * *
g6pd_202_rtpcrHOM/HEMI 6.968 0.9676 7.201 9.671e-12 * * *
age_at_collection_years_2010 0.7125 0.1067 6.677 2.014e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 5.273 0.3364 0.3272
  2.5 % 97.5 %
(Intercept) 64.88 68.52
g6pd_202_rtpcrHET 1.508 5.231
g6pd_202_rtpcrHOM/HEMI 5.061 8.875
age_at_collection_years_2010 0.5022 0.9228
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 72.16669 0.4410558 217 71.29739 73.03599
  • HET 75.53634 0.8356520 217 73.88931 77.18337
  • HOM/HEMI 79.13446 0.8603034 217 77.43884 80.83008
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.11 0.483 149.3 1.706e-221 * * *
g6pd_202_rtpcrHET 3.053 1.033 2.955 0.003467 * *
g6pd_202_rtpcrHOM/HEMI 7.636 1.054 7.243 7.465e-12 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 5.776 0.2001 0.1927
  2.5 % 97.5 %
(Intercept) 71.16 73.06
g6pd_202_rtpcrHET 1.017 5.09
g6pd_202_rtpcrHOM/HEMI 5.558 9.713
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 72.10909 0.4830417 218 71.15706 73.06112
  • HET 75.16250 0.9133192 218 73.36243 76.96257
  • HOM/HEMI 79.74474 0.9370458 218 77.89791 81.59157
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.23 0.9898 68.93 5.455e-150 * * *
sickleHET -2.108 1.082 -1.949 0.0526
age_at_collection_years_2010 0.7922 0.1178 6.723 1.534e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 5.854 0.1784 0.1709
  2.5 % 97.5 %
(Intercept) 66.28 70.18
sickleHET -4.239 0.02384
age_at_collection_years_2010 0.5599 1.024
  • sickle emmean SE df lower.CL upper.CL
  • NORM 74.30846 0.4294234 218 73.46211 75.15481
  • HET 72.20076 0.9917559 218 70.24610 74.15541
sickle n
NORM 186
HET 35

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.22 0.4706 157.7 1.779e-227 * * *
sickleHET -1.576 1.182 -1.332 0.1841
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 6.418 0.008042 0.003513
  2.5 % 97.5 %
(Intercept) 73.3 75.15
sickleHET -3.906 0.7548
  • sickle emmean SE df lower.CL upper.CL
  • NORM 74.22419 0.4705697 219 73.29677 75.15162
  • HET 72.64857 1.0847919 219 70.51060 74.78654
sickle n
NORM 186
HET 35

thal _ HOM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.41 1.359 45.92 1.159e-66 * * *
g6pd_202_rtpcrHET 2.728 1.486 1.836 0.06954
g6pd_202_rtpcrHOM/HEMI 3.16 1.598 1.978 0.05079
age_at_collection_years_2010 0.3258 0.1601 2.035 0.04463 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 4.75 0.1067 0.07845
  2.5 % 97.5 %
(Intercept) 59.72 65.11
g6pd_202_rtpcrHET -0.2223 5.678
g6pd_202_rtpcrHOM/HEMI -0.01112 6.332
age_at_collection_years_2010 0.007977 0.6435
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 64.98550 0.5415738 95 63.91034 66.06066
  • HET 67.71313 1.3816343 95 64.97024 70.45602
  • HOM/HEMI 68.14591 1.5037651 95 65.16056 71.13126
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 64.95 0.5501 118.1 9.786e-106 * * *
g6pd_202_rtpcrHET 3.108 1.498 2.074 0.04072 *
g6pd_202_rtpcrHOM/HEMI 3.049 1.623 1.879 0.06323
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 4.827 0.06771 0.04829
  2.5 % 97.5 %
(Intercept) 63.86 66.04
g6pd_202_rtpcrHET 0.134 6.081
g6pd_202_rtpcrHOM/HEMI -0.1713 6.27
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 64.95065 0.5500887 96 63.85873 66.04257
  • HET 68.05833 1.3934375 96 65.29238 70.82428
  • HOM/HEMI 68.00000 1.5264343 96 64.97005 71.02995
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.09 1.402 44.27 1.104e-65 * * *
sickleHET 2.656 1.284 2.068 0.04131 *
age_at_collection_years_2010 0.3918 0.1609 2.434 0.01676 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 4.779 0.08616 0.06712
  2.5 % 97.5 %
(Intercept) 59.3 64.87
sickleHET 0.1069 5.206
age_at_collection_years_2010 0.07234 0.7112
  • sickle emmean SE df lower.CL upper.CL
  • NORM 65.17919 0.528528 96 64.13007 66.22831
  • HET 67.83567 1.167255 96 65.51868 70.15265
sickle n
NORM 82
HET 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.25 0.541 120.6 1.711e-107 * * *
sickleHET 2.251 1.306 1.724 0.08782
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 4.899 0.02974 0.01974
  2.5 % 97.5 %
(Intercept) 64.18 66.32
sickleHET -0.3399 4.842
  • sickle emmean SE df lower.CL upper.CL
  • NORM 65.24878 0.5409907 97 64.17506 66.32250
  • HET 67.50000 1.1881528 97 65.14185 69.85815
sickle n
NORM 82
HET 17

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.68 0.4044 182.2 0 * * *
sickleHET -0.6597 0.999 -0.6604 0.5093
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 8.067 0.0009192 -0.001189
  2.5 % 97.5 %
(Intercept) 72.88 74.47
sickleHET -2.623 1.303
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.56 0.9125 75.13 4.58e-265 * * *
sickleHET -0.663 0.9617 -0.6894 0.4909
age_at_collection_years_2010 0.675 0.1088 6.204 1.202e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 7.766 0.0761 0.0722
  2.5 % 97.5 %
(Intercept) 66.76 70.35
sickleHET -2.553 1.227
age_at_collection_years_2010 0.4612 0.8888

sickle _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.54 0.9656 69.95 2.955e-224 * * *
g6pd_202_rtpcrHET 3.235 1.058 3.059 0.002375 * *
g6pd_202_rtpcrHOM/HEMI 6.865 1.082 6.344 6.164e-10 * * *
age_at_collection_years_2010 0.6117 0.1143 5.354 1.463e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 7.484 0.1641 0.1577
  2.5 % 97.5 %
(Intercept) 65.64 69.44
g6pd_202_rtpcrHET 1.156 5.315
g6pd_202_rtpcrHOM/HEMI 4.737 8.992
age_at_collection_years_2010 0.3871 0.8363
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 72.18136 0.4481737 394 71.30024 73.06247
  • HET 75.41671 0.9581808 394 73.53292 77.30050
  • HOM/HEMI 79.04625 0.9843362 394 77.11104 80.98146
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.12 0.4635 155.6 0 * * *
g6pd_202_rtpcrHET 3.279 1.094 2.997 0.002899 * *
g6pd_202_rtpcrHOM/HEMI 7.234 1.117 6.476 2.805e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 7.741 0.1033 0.09876
  2.5 % 97.5 %
(Intercept) 71.21 73.03
g6pd_202_rtpcrHET 1.128 5.43
g6pd_202_rtpcrHOM/HEMI 5.038 9.431
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 72.12079 0.4634563 395 71.20964 73.03194
  • HET 75.40000 0.9911650 395 73.45138 77.34862
  • HOM/HEMI 79.35517 1.0164754 395 77.35679 81.35355
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.52 0.8829 82.13 5.722e-250 * * *
thalHET -4.197 0.7114 -5.9 7.849e-09 * * *
thalHOM -13.57 0.8801 -15.41 2.788e-42 * * *
age_at_collection_years_2010 0.7801 0.09509 8.204 3.338e-15 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 6.219 0.4227 0.4183
  2.5 % 97.5 %
(Intercept) 70.78 74.25
thalHET -5.596 -2.799
thalHOM -15.3 -11.84
age_at_collection_years_2010 0.5931 0.967
  • thal emmean SE df lower.CL upper.CL
  • NORM 78.43412 0.5461445 394 77.36040 79.50784
  • HET 74.23680 0.4560289 394 73.34024 75.13335
  • HOM 64.86854 0.6883767 394 63.51519 66.22189
thal n
NORM 130
HET 186
HOM 82

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.21 0.5895 132.7 0 * * *
thalHET -3.988 0.7683 -5.191 3.362e-07 * * *
thalHOM -12.96 0.9478 -13.68 3.926e-35 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 6.721 0.3241 0.3206
  2.5 % 97.5 %
(Intercept) 77.05 79.37
thalHET -5.499 -2.478
thalHOM -14.83 -11.1
  • thal emmean SE df lower.CL upper.CL
  • NORM 78.21231 0.5894791 395 77.05340 79.37122
  • HET 74.22419 0.4928147 395 73.25533 75.19306
  • HOM 65.24878 0.7422209 395 63.78958 66.70798
thal n
NORM 130
HET 186
HOM 82

sickle _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.51 2.285 28.67 8.094e-42 * * *
g6pd_202_rtpcrHET 3.606 2.202 1.637 0.1058
g6pd_202_rtpcrHOM/HEMI 2.777 2.295 1.21 0.2301
age_at_collection_years_2010 0.8471 0.2609 3.247 0.001751 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 7.167 0.146 0.1114
  2.5 % 97.5 %
(Intercept) 60.96 70.07
g6pd_202_rtpcrHET -0.7819 7.993
g6pd_202_rtpcrHOM/HEMI -1.796 7.351
age_at_collection_years_2010 0.3274 1.367
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 71.94347 0.9969432 74 69.95702 73.92992
  • HET 75.54923 1.9492083 74 71.66534 79.43311
  • HOM/HEMI 74.72086 2.0708532 74 70.59460 78.84713
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.19 1.055 68.41 2.444e-69 * * *
g6pd_202_rtpcrHET 2.184 2.291 0.9533 0.3435
g6pd_202_rtpcrHOM/HEMI 2.806 2.437 1.151 0.2533
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 7.61 0.02434 -0.00168
  2.5 % 97.5 %
(Intercept) 70.09 74.3
g6pd_202_rtpcrHET -2.38 6.749
g6pd_202_rtpcrHOM/HEMI -2.049 7.661
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 72.19423 1.055310 75 70.09194 74.29652
  • HET 74.37857 2.033845 75 70.32694 78.43020
  • HOM/HEMI 75.00000 2.196804 75 70.62374 79.37626
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.36 2.051 34.79 1.325e-47 * * *
thalHET -5.425 1.666 -3.255 0.00171 * *
thalHOM -9.593 1.981 -4.842 6.862e-06 * * *
age_at_collection_years_2010 0.8151 0.2303 3.539 0.0006983 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 6.352 0.3293 0.3021
  2.5 % 97.5 %
(Intercept) 67.27 75.44
thalHET -8.745 -2.104
thalHOM -13.54 -5.645
age_at_collection_years_2010 0.3562 1.274
  • thal emmean SE df lower.CL upper.CL
  • NORM 77.54282 1.251378 74 75.04939 80.03624
  • HET 72.11828 1.084103 74 69.95816 74.27840
  • HOM 67.94981 1.545839 74 64.86966 71.02997
thal n
NORM 26
HET 35
HOM 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.12 1.338 57.64 7.33e-64 * * *
thalHET -4.475 1.766 -2.533 0.0134 *
thalHOM -9.623 2.128 -4.522 2.25e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 6.823 0.2158 0.1949
  2.5 % 97.5 %
(Intercept) 74.46 79.79
thalHET -7.993 -0.9556
thalHOM -13.86 -5.384
  • thal emmean SE df lower.CL upper.CL
  • NORM 77.12308 1.338034 75 74.45758 79.78858
  • HET 72.64857 1.153240 75 70.35120 74.94594
  • HOM 67.50000 1.654739 75 64.20359 70.79641
thal n
NORM 26
HET 35
HOM 17

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

mch_2010

####All_vs_g6pd+thal________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.9 0.2168 119.4 0 * * *
g6pd_202_rtpcrHET 0.7558 0.3268 2.313 0.02117 *
g6pd_202_rtpcrHOM/HEMI 1.774 0.3365 5.272 2.061e-07 * * *
thalHET -1.785 0.2669 -6.688 6.423e-11 * * *
thalHOM -4.92 0.3278 -15.01 6.801e-42 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 2.547 0.36 0.3546
  2.5 % 97.5 %
(Intercept) 25.47 26.33
g6pd_202_rtpcrHET 0.1136 1.398
g6pd_202_rtpcrHOM/HEMI 1.113 2.435
thalHET -2.309 -1.261
thalHOM -5.564 -4.275
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 25.89929 0.2168404 471 25.47319 26.32538
  • HET NORM 26.65514 0.3414487 471 25.98419 27.32609
  • HOM/HEMI NORM 27.67323 0.3497092 471 26.98605 28.36041
  • NORM HET 24.11428 0.1935964 471 23.73386 24.49470
  • HET HET 24.87013 0.3140320 471 24.25305 25.48721
  • HOM/HEMI HET 25.88822 0.3230872 471 25.25335 26.52309
  • NORM HOM 20.97970 0.2622443 471 20.46439 21.49501
  • HET HOM 21.73555 0.3815419 471 20.98582 22.48529
  • HOM/HEMI HOM 22.75364 0.3926580 471 21.98206 23.52522
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.76 0.3129 75.95 4.833e-266 * * *
g6pd_202_rtpcrHET 0.8259 0.3028 2.728 0.006612 * *
g6pd_202_rtpcrHOM/HEMI 1.619 0.3121 5.186 3.192e-07 * * *
thalHET -1.904 0.2475 -7.694 8.461e-14 * * *
thalHOM -5.111 0.3043 -16.79 6.319e-50 * * *
age_at_collection_years_2010 0.2956 0.03321 8.901 1.211e-17 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 2.359 0.4524 0.4465
  2.5 % 97.5 %
(Intercept) 23.15 24.38
g6pd_202_rtpcrHET 0.231 1.421
g6pd_202_rtpcrHOM/HEMI 1.005 2.232
thalHET -2.391 -1.418
thalHOM -5.709 -4.513
age_at_collection_years_2010 0.2303 0.3609
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 26.00626 0.2011656 470 25.61097 26.40156
  • HET NORM 26.83218 0.3168254 470 26.20961 27.45475
  • HOM/HEMI NORM 27.62495 0.3238955 470 26.98849 28.26141
  • NORM HET 24.10186 0.1792864 470 23.74956 24.45417
  • HET HET 24.92778 0.2908831 470 24.35619 25.49937
  • HOM/HEMI HET 25.72055 0.2997891 470 25.13146 26.30964
  • NORM HOM 20.89555 0.2430367 470 20.41797 21.37312
  • HET HOM 21.72146 0.3533325 470 21.02716 22.41577
  • HOM/HEMI HOM 22.51423 0.3646166 470 21.79775 23.23071
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

####All_vs_g6pd+sickle________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.05 0.1802 133.5 0 * * *
g6pd_202_rtpcrHET 0.9455 0.3953 2.392 0.01716 *
g6pd_202_rtpcrHOM/HEMI 2.024 0.4065 4.979 8.982e-07 * * *
sickleHET -0.4449 0.3828 -1.162 0.2457
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.09 0.05641 0.05041
  2.5 % 97.5 %
(Intercept) 23.7 24.41
g6pd_202_rtpcrHET 0.1687 1.722
g6pd_202_rtpcrHOM/HEMI 1.225 2.823
sickleHET -1.197 0.3072
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 24.05359 0.1801724 472 23.69955 24.40763
  • HET NORM 24.99906 0.3638839 472 24.28402 25.71409
  • HOM/HEMI NORM 26.07770 0.3751069 472 25.34062 26.81479
  • NORM HET 23.60864 0.3646263 472 22.89215 24.32513
  • HET HET 24.55411 0.4735365 472 23.62361 25.48461
  • HOM/HEMI HET 25.63276 0.4868225 472 24.67615 26.58937
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.12 0.36 61.43 5.343e-227 * * *
g6pd_202_rtpcrHET 1.008 0.3809 2.646 0.008423 * *
g6pd_202_rtpcrHOM/HEMI 1.893 0.3922 4.826 1.883e-06 * * *
sickleHET -0.4468 0.3687 -1.212 0.2262
age_at_collection_years_2010 0.2565 0.04179 6.139 1.768e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 2.976 0.1263 0.1189
  2.5 % 97.5 %
(Intercept) 21.41 22.82
g6pd_202_rtpcrHET 0.2593 1.756
g6pd_202_rtpcrHOM/HEMI 1.122 2.663
sickleHET -1.171 0.2778
age_at_collection_years_2010 0.1744 0.3386
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 24.06336 0.1735616 471 23.72231 24.40441
  • HET NORM 25.07124 0.3507148 471 24.38208 25.76040
  • HOM/HEMI NORM 25.95618 0.3618703 471 25.24510 26.66726
  • NORM HET 23.61658 0.3512352 471 22.92640 24.30677
  • HET HET 24.62446 0.4562864 471 23.72785 25.52107
  • HOM/HEMI HET 25.50940 0.4693709 471 24.58708 26.43172
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.98 0.1699 141.2 0 * * *
g6pd_202_rtpcrHET 0.9323 0.3953 2.358 0.01876 *
g6pd_202_rtpcrHOM/HEMI 2.018 0.4067 4.962 9.759e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.091 0.05371 0.0497
  2.5 % 97.5 %
(Intercept) 23.65 24.32
g6pd_202_rtpcrHET 0.1555 1.709
g6pd_202_rtpcrHOM/HEMI 1.219 2.817
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.05 0.3556 62.01 5.493e-229 * * *
g6pd_202_rtpcrHET 0.9947 0.381 2.611 0.009319 * *
g6pd_202_rtpcrHOM/HEMI 1.886 0.3923 4.808 2.053e-06 * * *
age_at_collection_years_2010 0.2565 0.04181 6.135 1.807e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 2.978 0.1236 0.118
  2.5 % 97.5 %
(Intercept) 21.35 22.75
g6pd_202_rtpcrHET 0.2461 1.743
g6pd_202_rtpcrHOM/HEMI 1.115 2.657
age_at_collection_years_2010 0.1743 0.3386

g6pd_202_rtpcr _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.6 0.3676 64.21 1.909e-187 * * *
thalHET -2.115 0.2996 -7.059 1.01e-11 * * *
thalHOM -5.032 0.3515 -14.31 2.03e-36 * * *
age_at_collection_years_2010 0.3267 0.03971 8.226 4.622e-15 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 2.367 0.4438 0.4387
  2.5 % 97.5 %
(Intercept) 22.88 24.32
thalHET -2.704 -1.526
thalHOM -5.723 -4.34
age_at_collection_years_2010 0.2486 0.4048
  • thal emmean SE df lower.CL upper.CL
  • NORM 26.06794 0.2248432 327 25.62561 26.51026
  • HET 23.95295 0.1979473 327 23.56354 24.34236
  • HOM 21.03620 0.2699124 327 20.50522 21.56718
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.99 0.2464 105.5 3.857e-255 * * *
thalHET -2.027 0.3284 -6.171 2.003e-09 * * *
thalHOM -4.88 0.3851 -12.67 3.128e-30 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 2.596 0.3287 0.3246
  2.5 % 97.5 %
(Intercept) 25.51 26.48
thalHET -2.673 -1.381
thalHOM -5.638 -4.123
  • thal emmean SE df lower.CL upper.CL
  • NORM 25.99459 0.2464449 328 25.50978 26.47941
  • HET 23.96783 0.2171267 328 23.54070 24.39497
  • HOM 21.11429 0.2958939 328 20.53220 21.69637
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.77 0.4192 51.93 2.737e-160 * * *
sickleHET -0.2024 0.4556 -0.4443 0.6571
age_at_collection_years_2010 0.2978 0.05053 5.894 9.379e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 3.013 0.09584 0.09033
  2.5 % 97.5 %
(Intercept) 20.94 22.59
sickleHET -1.099 0.6939
age_at_collection_years_2010 0.1984 0.3972
  • sickle emmean SE df lower.CL upper.CL
  • NORM 24.01549 0.1804346 328 23.66053 24.37044
  • HET 23.81306 0.4182285 328 22.99031 24.63581
sickle n
NORM 279
HET 52

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24 0.1894 126.7 3.067e-281 * * *
sickleHET -0.08331 0.4779 -0.1743 0.8617
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 3.164 9.236e-05 -0.002947
  2.5 % 97.5 %
(Intercept) 23.62 24.37
sickleHET -1.023 0.8569
  • sickle emmean SE df lower.CL upper.CL
  • NORM 23.99677 0.1894301 329 23.62413 24.36942
  • HET 23.91346 0.4387827 329 23.05029 24.77664
sickle n
NORM 279
HET 52

g6pd_202_rtpcr _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25 0.7427 33.66 2.22e-45 * * *
thalHET -1.889 0.6077 -3.109 0.002699 * *
thalHOM -5.254 0.8466 -6.207 3.224e-08 * * *
age_at_collection_years_2010 0.2416 0.08397 2.878 0.00529 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 2.318 0.366 0.3392
  2.5 % 97.5 %
(Intercept) 23.52 26.48
thalHET -3.101 -0.6777
thalHOM -6.942 -3.566
age_at_collection_years_2010 0.07419 0.409
  • thal emmean SE df lower.CL upper.CL
  • NORM 26.76436 0.4858482 71 25.79560 27.73311
  • HET 24.87501 0.3667224 71 24.14378 25.60623
  • HOM 21.50996 0.6832894 71 20.14752 22.87240
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.62 0.5072 52.49 3.902e-59 * * *
thalHET -1.784 0.6365 -2.803 0.006496 * *
thalHOM -4.713 0.8661 -5.442 6.917e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 2.432 0.2921 0.2724
  2.5 % 97.5 %
(Intercept) 25.61 27.63
thalHET -3.053 -0.5154
thalHOM -6.44 -2.987
  • thal emmean SE df lower.CL upper.CL
  • NORM 26.62174 0.5071601 72 25.61073 27.63274
  • HET 24.83750 0.3845732 72 24.07087 25.60413
  • HOM 21.90833 0.7021314 72 20.50866 23.30801
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.14 0.8532 28.29 9.694e-41 * * *
sickleHET -0.4575 0.8569 -0.5339 0.5951
age_at_collection_years_2010 0.1181 0.102 1.158 0.2507
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 2.854 0.02535 -0.001722
  2.5 % 97.5 %
(Intercept) 22.44 25.84
sickleHET -2.166 1.251
age_at_collection_years_2010 -0.08522 0.3214
  • sickle emmean SE df lower.CL upper.CL
  • NORM 25.00139 0.3663026 72 24.27118 25.7316
  • HET 24.54393 0.7709020 72 23.00717 26.0807
sickle n
NORM 61
HET 14

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.03 0.3663 68.34 6.373e-68 * * *
sickleHET -0.6169 0.8477 -0.7277 0.4691
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 2.861 0.007201 -0.006399
  2.5 % 97.5 %
(Intercept) 24.3 25.76
sickleHET -2.306 1.073
  • sickle emmean SE df lower.CL upper.CL
  • NORM 25.03115 0.3662521 73 24.30121 25.76109
  • HET 24.41429 0.7645061 73 22.89063 25.93794
sickle n
NORM 61
HET 14

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.91 0.8342 31.06 4.096e-41 * * *
thalHET -0.929 0.6316 -1.471 0.1461
thalHOM -5.703 0.8902 -6.407 1.808e-08 * * *
age_at_collection_years_2010 0.1745 0.08895 1.962 0.05402
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 2.334 0.4236 0.3974
  2.5 % 97.5 %
(Intercept) 24.25 27.58
thalHET -2.19 0.332
thalHOM -7.481 -3.926
age_at_collection_years_2010 -0.003105 0.3521
  • thal emmean SE df lower.CL upper.CL
  • NORM 27.32054 0.4998671 66 26.32253 28.31856
  • HET 26.39150 0.3809284 66 25.63095 27.15205
  • HOM 21.61711 0.7400781 66 20.13950 23.09473
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.23 0.5081 53.59 9.584e-57 * * *
thalHET -0.7536 0.6384 -1.18 0.242
thalHOM -5.717 0.9089 -6.291 2.753e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 2.383 0.39 0.3718
  2.5 % 97.5 %
(Intercept) 26.21 28.24
thalHET -2.028 0.5207
thalHOM -7.531 -3.903
  • thal emmean SE df lower.CL upper.CL
  • NORM 27.22727 0.5080667 67 26.21317 28.24138
  • HET 26.47368 0.3865808 67 25.70207 27.24530
  • HOM 21.51000 0.7535848 67 20.00584 23.01416
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.73 0.9607 25.74 1.853e-36 * * *
sickleHET -1.809 0.9193 -1.967 0.05327
age_at_collection_years_2010 0.1962 0.109 1.799 0.0765
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 2.898 0.09769 0.07076
  2.5 % 97.5 %
(Intercept) 22.81 26.65
sickleHET -3.644 0.02624
age_at_collection_years_2010 -0.02147 0.4138
  • sickle emmean SE df lower.CL upper.CL
  • NORM 26.31151 0.3805807 67 25.55186 27.07115
  • HET 24.50272 0.8368133 67 22.83244 26.17301
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.32 0.3868 68.04 2.806e-64 * * *
sickleHET -1.842 0.9342 -1.972 0.05267
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 2.946 0.0541 0.04019
  2.5 % 97.5 %
(Intercept) 25.55 27.09
sickleHET -3.706 0.02183
  • sickle emmean SE df lower.CL upper.CL
  • NORM 26.31724 0.3867760 68 25.54544 27.08904
  • HET 24.47500 0.8503209 68 22.77821 26.17179
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.26 0.2098 125.2 0 * * *
thalHET -1.705 0.2741 -6.221 1.091e-09 * * *
thalHOM -5.01 0.3368 -14.88 2.398e-41 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 2.621 0.3198 0.3169
  2.5 % 97.5 %
(Intercept) 25.85 26.67
thalHET -2.243 -1.166
thalHOM -5.672 -4.349
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.06 0.315 76.39 7.319e-268 * * *
thalHET -1.83 0.2543 -7.194 2.484e-12 * * *
thalHOM -5.202 0.3128 -16.63 3.226e-49 * * *
age_at_collection_years_2010 0.303 0.03411 8.883 1.369e-17 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 2.428 0.4172 0.4135
  2.5 % 97.5 %
(Intercept) 23.44 24.68
thalHET -2.329 -1.33
thalHOM -5.816 -4.587
age_at_collection_years_2010 0.236 0.37

thal _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.16 0.5782 40.05 1.155e-82 * * *
g6pd_202_rtpcrHET 0.8628 0.6561 1.315 0.1905
g6pd_202_rtpcrHOM/HEMI 1.155 0.6671 1.731 0.08553
age_at_collection_years_2010 0.3871 0.06971 5.552 1.226e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 2.858 0.1858 0.1697
  2.5 % 97.5 %
(Intercept) 22.02 24.3
g6pd_202_rtpcrHET -0.4334 2.159
g6pd_202_rtpcrHOM/HEMI -0.1634 2.472
age_at_collection_years_2010 0.2493 0.5248
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 25.97087 0.2712754 152 25.43491 26.50683
  • HET 26.83368 0.5970949 152 25.65400 28.01336
  • HOM/HEMI 27.12539 0.6095419 152 25.92112 28.32966
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.99 0.2965 87.67 1.075e-132 * * *
g6pd_202_rtpcrHET 0.6271 0.7157 0.8763 0.3822
g6pd_202_rtpcrHOM/HEMI 1.233 0.729 1.691 0.0929
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 3.124 0.0206 0.007802
  2.5 % 97.5 %
(Intercept) 25.41 26.58
g6pd_202_rtpcrHET -0.7868 2.041
g6pd_202_rtpcrHOM/HEMI -0.2076 2.673
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 25.99459 0.2965065 153 25.40882 26.58037
  • HET 26.62174 0.6513761 153 25.33489 27.90859
  • HOM/HEMI 27.22727 0.6660156 153 25.91150 28.54305
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.57 0.5706 41.31 7.104e-85 * * *
sickleHET -0.5405 0.6187 -0.8735 0.3838
age_at_collection_years_2010 0.383 0.07007 5.466 1.83e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 2.879 0.1681 0.1572
  2.5 % 97.5 %
(Intercept) 22.44 24.7
sickleHET -1.763 0.6819
age_at_collection_years_2010 0.2446 0.5215
  • sickle emmean SE df lower.CL upper.CL
  • NORM 26.35098 0.2525323 153 25.85208 26.84988
  • HET 25.81051 0.5648023 153 24.69469 26.92632
sickle n
NORM 130
HET 26

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.37 0.2752 95.81 3.59e-139 * * *
sickleHET -0.6269 0.674 -0.9301 0.3538
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 3.138 0.005586 -0.0008713
  2.5 % 97.5 %
(Intercept) 25.82 26.91
sickleHET -1.958 0.7046
  • sickle emmean SE df lower.CL upper.CL
  • NORM 26.36538 0.2751782 154 25.82177 26.90900
  • HET 25.73846 0.6153173 154 24.52291 26.95401
sickle n
NORM 130
HET 26

thal _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.82 0.3799 57.43 3.266e-133 * * *
g6pd_202_rtpcrHET 0.9952 0.3889 2.559 0.01116 *
g6pd_202_rtpcrHOM/HEMI 2.241 0.3984 5.624 5.708e-08 * * *
age_at_collection_years_2010 0.2829 0.04394 6.439 7.626e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 2.171 0.2749 0.2649
  2.5 % 97.5 %
(Intercept) 21.07 22.57
g6pd_202_rtpcrHET 0.2288 1.762
g6pd_202_rtpcrHOM/HEMI 1.455 3.026
age_at_collection_years_2010 0.1963 0.3695
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 23.99070 0.1816040 217 23.63277 24.34864
  • HET 24.98594 0.3440784 217 24.30778 25.66410
  • HOM/HEMI 26.23136 0.3542285 217 25.53319 26.92953
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.97 0.1977 121.2 5.025e-202 * * *
g6pd_202_rtpcrHET 0.8697 0.4229 2.057 0.04092 *
g6pd_202_rtpcrHOM/HEMI 2.506 0.4315 5.808 2.223e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 2.364 0.1364 0.1284
  2.5 % 97.5 %
(Intercept) 23.58 24.36
g6pd_202_rtpcrHET 0.03623 1.703
g6pd_202_rtpcrHOM/HEMI 1.655 3.356
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 23.96783 0.1977012 218 23.57818 24.35748
  • HET 24.83750 0.3738069 218 24.10076 25.57424
  • HOM/HEMI 26.47368 0.3835178 218 25.71781 27.22956
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.32 0.3893 57.33 1.895e-133 * * *
sickleHET -0.9446 0.4253 -2.221 0.02739 *
age_at_collection_years_2010 0.3114 0.04634 6.72 1.561e-10 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 2.302 0.181 0.1735
  2.5 % 97.5 %
(Intercept) 21.55 23.08
sickleHET -1.783 -0.1063
age_at_collection_years_2010 0.2201 0.4027
  • sickle emmean SE df lower.CL upper.CL
  • NORM 24.70571 0.1688848 218 24.37285 25.03856
  • HET 23.76111 0.3900404 218 22.99237 24.52984
sickle n
NORM 186
HET 35

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.67 0.1851 133.3 1.182e-211 * * *
sickleHET -0.7354 0.465 -1.582 0.1152
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 2.524 0.01129 0.006778
  2.5 % 97.5 %
(Intercept) 24.31 25.04
sickleHET -1.652 0.181
  • sickle emmean SE df lower.CL upper.CL
  • NORM 24.67258 0.1850525 219 24.30787 25.03729
  • HET 23.93714 0.4265965 219 23.09638 24.77790
sickle n
NORM 186
HET 35

thal _ HOM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.02 0.4989 40.13 2.259e-61 * * *
g6pd_202_rtpcrHET 0.6304 0.5455 1.156 0.2507
g6pd_202_rtpcrHOM/HEMI 0.4435 0.5864 0.7564 0.4513
age_at_collection_years_2010 0.1403 0.05876 2.387 0.01895 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 1.744 0.07856 0.04946
  2.5 % 97.5 %
(Intercept) 19.03 21.01
g6pd_202_rtpcrHET -0.4525 1.713
g6pd_202_rtpcrHOM/HEMI -0.7206 1.608
age_at_collection_years_2010 0.02363 0.2569
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 21.12929 0.1987981 95 20.73463 21.52396
  • HET 21.75968 0.5071632 95 20.75283 22.76653
  • HOM/HEMI 21.57283 0.5519944 95 20.47698 22.66868
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.11 0.2035 103.8 2.184e-100 * * *
g6pd_202_rtpcrHET 0.794 0.5542 1.433 0.1552
g6pd_202_rtpcrHOM/HEMI 0.3957 0.6003 0.6592 0.5113
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 1.786 0.02327 0.002927
  2.5 % 97.5 %
(Intercept) 20.71 21.52
g6pd_202_rtpcrHET -0.3061 1.894
g6pd_202_rtpcrHOM/HEMI -0.7958 1.587
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 21.11429 0.2035044 96 20.71033 21.51824
  • HET 21.90833 0.5154997 96 20.88507 22.93159
  • HOM/HEMI 21.51000 0.5647017 96 20.38908 22.63092
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.81 0.503 39.39 4.638e-61 * * *
sickleHET 0.9305 0.4607 2.02 0.04618 *
age_at_collection_years_2010 0.1621 0.05772 2.809 0.00603 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 1.714 0.1001 0.08134
  2.5 % 97.5 %
(Intercept) 18.81 20.81
sickleHET 0.01609 1.845
age_at_collection_years_2010 0.04754 0.2767
  • sickle emmean SE df lower.CL upper.CL
  • NORM 21.09072 0.1895671 96 20.71443 21.46701
  • HET 22.02124 0.4186592 96 21.19021 22.85228
sickle n
NORM 82
HET 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.12 0.1959 107.8 8.404e-103 * * *
sickleHET 0.7628 0.4727 1.614 0.1098
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 1.774 0.02614 0.0161
  2.5 % 97.5 %
(Intercept) 20.73 21.51
sickleHET -0.1754 1.701
  • sickle emmean SE df lower.CL upper.CL
  • NORM 21.11951 0.195895 97 20.73071 21.50831
  • HET 21.88235 0.430235 97 21.02846 22.73625
sickle n
NORM 82
HET 17

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.49 0.1589 154.1 0 * * *
sickleHET -0.4037 0.3926 -1.028 0.3043
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.171 0.002226 0.0001207
  2.5 % 97.5 %
(Intercept) 24.18 24.81
sickleHET -1.175 0.3678
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.49 0.3587 62.68 2.899e-231 * * *
sickleHET -0.405 0.378 -1.071 0.2845
age_at_collection_years_2010 0.2647 0.04277 6.19 1.308e-09 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 3.053 0.07699 0.07309
  2.5 % 97.5 %
(Intercept) 21.78 23.19
sickleHET -1.148 0.3378
age_at_collection_years_2010 0.1807 0.3488

sickle _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.17 0.3874 57.24 5.065e-193 * * *
g6pd_202_rtpcrHET 1.017 0.4244 2.396 0.01703 *
g6pd_202_rtpcrHOM/HEMI 2.173 0.4341 5.006 8.401e-07 * * *
age_at_collection_years_2010 0.2437 0.04583 5.318 1.764e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 3.002 0.1308 0.1242
  2.5 % 97.5 %
(Intercept) 21.41 22.93
g6pd_202_rtpcrHET 0.1826 1.851
g6pd_202_rtpcrHOM/HEMI 1.32 3.027
age_at_collection_years_2010 0.1536 0.3339
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 24.02091 0.1797959 394 23.66743 24.37439
  • HET 25.03781 0.3843979 394 24.28208 25.79353
  • HOM/HEMI 26.19415 0.3948908 394 25.41779 26.97051
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24 0.1858 129.1 0 * * *
g6pd_202_rtpcrHET 1.034 0.4388 2.358 0.01888 *
g6pd_202_rtpcrHOM/HEMI 2.32 0.448 5.18 3.544e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 3.104 0.06838 0.06367
  2.5 % 97.5 %
(Intercept) 23.63 24.36
g6pd_202_rtpcrHET 0.1718 1.897
g6pd_202_rtpcrHOM/HEMI 1.44 3.201
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 23.99677 0.1858416 395 23.63141 24.36214
  • HET 25.03115 0.3974478 395 24.24977 25.81253
  • HOM/HEMI 26.31724 0.4075970 395 25.51591 27.11857
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.12 0.3419 70.54 1.359e-225 * * *
thalHET -1.775 0.2755 -6.444 3.401e-10 * * *
thalHOM -5.483 0.3408 -16.09 3.976e-45 * * *
age_at_collection_years_2010 0.3078 0.03682 8.358 1.108e-15 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 2.408 0.4406 0.4364
  2.5 % 97.5 %
(Intercept) 23.45 24.79
thalHET -2.317 -1.234
thalHOM -6.153 -4.813
age_at_collection_years_2010 0.2354 0.3802
  • thal emmean SE df lower.CL upper.CL
  • NORM 26.45290 0.2114921 394 26.03710 26.86869
  • HET 24.67755 0.1765952 394 24.33037 25.02474
  • HOM 20.96950 0.2665709 394 20.44542 21.49358
thal n
NORM 130
HET 186
HOM 82

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.37 0.2289 115.2 4.741e-306 * * *
thalHET -1.693 0.2984 -5.674 2.706e-08 * * *
thalHOM -5.246 0.3681 -14.25 1.756e-37 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 2.61 0.3414 0.3381
  2.5 % 97.5 %
(Intercept) 25.92 26.82
thalHET -2.279 -1.106
thalHOM -5.969 -4.522
  • thal emmean SE df lower.CL upper.CL
  • NORM 26.36538 0.2289042 395 25.91536 26.81541
  • HET 24.67258 0.1913679 395 24.29635 25.04881
  • HOM 21.11951 0.2882163 395 20.55288 21.68614
thal n
NORM 130
HET 186
HOM 82

sickle _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.36 0.9046 23.61 3.451e-36 * * *
g6pd_202_rtpcrHET 1.044 0.8718 1.197 0.235
g6pd_202_rtpcrHOM/HEMI 0.5507 0.9088 0.606 0.5464
age_at_collection_years_2010 0.3237 0.1033 3.134 0.002473 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 2.838 0.1235 0.08796
  2.5 % 97.5 %
(Intercept) 19.56 23.16
g6pd_202_rtpcrHET -0.6932 2.781
g6pd_202_rtpcrHOM/HEMI -1.26 2.362
age_at_collection_years_2010 0.1179 0.5295
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 23.81765 0.3947058 74 23.03119 24.60412
  • HET 24.86155 0.7717227 74 23.32386 26.39924
  • HOM/HEMI 24.36835 0.8198839 74 22.73470 26.00201
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.91 0.416 57.48 8.959e-64 * * *
g6pd_202_rtpcrHET 0.5008 0.9033 0.5545 0.5809
g6pd_202_rtpcrHOM/HEMI 0.5615 0.9608 0.5845 0.5607
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 3 0.007166 -0.01931
  2.5 % 97.5 %
(Intercept) 23.08 24.74
g6pd_202_rtpcrHET -1.299 2.3
g6pd_202_rtpcrHOM/HEMI -1.352 2.475
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 23.91346 0.4160191 75 23.08471 24.74221
  • HET 24.41429 0.8017720 75 22.81707 26.01150
  • HOM/HEMI 24.47500 0.8660127 75 22.74981 26.20019
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.49 0.7952 29.54 1.063e-42 * * *
thalHET -2.172 0.646 -3.362 0.001228 * *
thalHOM -3.844 0.7681 -5.005 3.658e-06 * * *
age_at_collection_years_2010 0.3178 0.08928 3.559 0.000654 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 2.462 0.34 0.3132
  2.5 % 97.5 %
(Intercept) 21.91 25.07
thalHET -3.459 -0.8846
thalHOM -5.375 -2.314
age_at_collection_years_2010 0.1399 0.4957
  • thal emmean SE df lower.CL upper.CL
  • NORM 25.90211 0.4851118 74 24.93550 26.86872
  • HET 23.73039 0.4202653 74 22.89300 24.56779
  • HOM 22.05773 0.5992631 74 20.86367 23.25178
thal n
NORM 26
HET 35
HOM 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.74 0.5191 49.58 4.436e-59 * * *
thalHET -1.801 0.6853 -2.628 0.0104 *
thalHOM -3.856 0.8256 -4.67 1.292e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 2.647 0.227 0.2064
  2.5 % 97.5 %
(Intercept) 24.7 26.77
thalHET -3.167 -0.436
thalHOM -5.501 -2.211
  • thal emmean SE df lower.CL upper.CL
  • NORM 25.73846 0.5191354 75 24.70429 26.77263
  • HET 23.93714 0.4474384 75 23.04580 24.82849
  • HOM 21.88235 0.6420115 75 20.60340 23.16131
thal n
NORM 26
HET 35
HOM 17

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

mchc_2010

####All_vs_g6pd+thal________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.66 0.08381 401.6 0 * * *
g6pd_202_rtpcrHET -0.1706 0.1263 -1.351 0.1775
g6pd_202_rtpcrHOM/HEMI -0.2327 0.1301 -1.789 0.07428
thalHET -0.4353 0.1032 -4.22 2.93e-05 * * *
thalHOM -1.261 0.1267 -9.951 2.653e-21 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.9846 0.1774 0.1704
  2.5 % 97.5 %
(Intercept) 33.5 33.83
g6pd_202_rtpcrHET -0.4189 0.07761
g6pd_202_rtpcrHOM/HEMI -0.4882 0.02291
thalHET -0.6381 -0.2326
thalHOM -1.51 -1.012
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 33.66053 0.0838134 471 33.49584 33.82522
  • HET NORM 33.48991 0.1319772 471 33.23057 33.74925
  • HOM/HEMI NORM 33.42787 0.1351701 471 33.16226 33.69348
  • NORM HET 33.22518 0.0748291 471 33.07814 33.37222
  • HET HET 33.05457 0.1213800 471 32.81605 33.29308
  • HOM/HEMI HET 32.99253 0.1248801 471 32.74714 33.23792
  • NORM HOM 32.39974 0.1013630 471 32.20056 32.59892
  • HET HOM 32.22912 0.1474741 471 31.93933 32.51891
  • HOM/HEMI HOM 32.16708 0.1517707 471 31.86885 32.46531
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.12 0.1266 261.6 0 * * *
g6pd_202_rtpcrHET -0.1528 0.1225 -1.248 0.2128
g6pd_202_rtpcrHOM/HEMI -0.2721 0.1263 -2.155 0.03169 *
thalHET -0.4657 0.1001 -4.65 4.324e-06 * * *
thalHOM -1.309 0.1231 -10.63 8.197e-24 * * *
age_at_collection_years_2010 0.07508 0.01344 5.587 3.913e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(3, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.9545 0.2286 0.2204
  2.5 % 97.5 %
(Intercept) 32.87 33.37
g6pd_202_rtpcrHET -0.3935 0.08789
g6pd_202_rtpcrHOM/HEMI -0.5202 -0.02395
thalHET -0.6625 -0.2689
thalHOM -1.551 -1.067
age_at_collection_years_2010 0.04867 0.1015
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 33.68770 0.0813931 470 33.52776 33.84764
  • HET NORM 33.53488 0.1281900 470 33.28298 33.78677
  • HOM/HEMI NORM 33.41561 0.1310506 470 33.15809 33.67313
  • NORM HET 33.22203 0.0725406 470 33.07949 33.36457
  • HET HET 33.06921 0.1176935 470 32.83794 33.30048
  • HOM/HEMI HET 32.94994 0.1212970 470 32.71159 33.18829
  • NORM HOM 32.37836 0.0983345 470 32.18513 32.57159
  • HET HOM 32.22554 0.1429610 470 31.94462 32.50646
  • HOM/HEMI HOM 32.10627 0.1475267 470 31.81638 32.39617
g6pd_202_rtpcr thal n
NORM NORM 111
NORM HET 143
NORM HOM 77
HET NORM 23
HET HET 40
HET HOM 12
HOM/HEMI NORM 22
HOM/HEMI HET 38
HOM/HEMI HOM 10

####All_vs_g6pd+sickle________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.22 0.0629 528.1 0 * * *
g6pd_202_rtpcrHET -0.1161 0.138 -0.8416 0.4004
g6pd_202_rtpcrHOM/HEMI -0.1643 0.1419 -1.158 0.2475
sickleHET -0.2369 0.1336 -1.773 0.07691
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 4, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.079 0.01052 0.004231
  2.5 % 97.5 %
(Intercept) 33.09 33.34
g6pd_202_rtpcrHET -0.3873 0.155
g6pd_202_rtpcrHOM/HEMI -0.4432 0.1146
sickleHET -0.4995 0.02569
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 33.21637 0.0629004 472 33.09277 33.33997
  • HET NORM 33.10022 0.1270363 472 32.85060 33.34985
  • HOM/HEMI NORM 33.05204 0.1309544 472 32.79472 33.30937
  • NORM HET 32.97946 0.1272955 472 32.72933 33.22960
  • HET HET 32.86331 0.1653174 472 32.53846 33.18816
  • HOM/HEMI HET 32.81513 0.1699557 472 32.48117 33.14910
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.72 0.128 255.6 0 * * *
g6pd_202_rtpcrHET -0.1003 0.1355 -0.7404 0.4594
g6pd_202_rtpcrHOM/HEMI -0.1977 0.1395 -1.417 0.157
sickleHET -0.2374 0.1311 -1.81 0.0709
age_at_collection_years_2010 0.06516 0.01486 4.385 1.435e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(2, 5, length(b))]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.059 0.04932 0.04125
  2.5 % 97.5 %
(Intercept) 32.47 32.98
g6pd_202_rtpcrHET -0.3665 0.1659
g6pd_202_rtpcrHOM/HEMI -0.4717 0.07638
sickleHET -0.495 0.0203
age_at_collection_years_2010 0.03596 0.09436
  • g6pd_202_rtpcr sickle emmean SE df lower.CL upper.CL
  • NORM NORM 33.21886 0.0617228 471 33.09757 33.34014
  • HET NORM 33.11856 0.1247228 471 32.87348 33.36364
  • HOM/HEMI NORM 33.02117 0.1286900 471 32.76830 33.27405
  • NORM HET 32.98148 0.1249078 471 32.73603 33.22693
  • HET HET 32.88118 0.1622666 471 32.56233 33.20004
  • HOM/HEMI HET 32.78380 0.1669198 471 32.45580 33.11180
g6pd_202_rtpcr sickle n
NORM NORM 279
NORM HET 52
HET NORM 61
HET HET 14
HOM/HEMI NORM 58
HOM/HEMI HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.18 0.05943 558.3 0 * * *
g6pd_202_rtpcrHET -0.1232 0.1383 -0.8907 0.3735
g6pd_202_rtpcrHOM/HEMI -0.1677 0.1422 -1.179 0.2389
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.081 0.003931 -0.0002804
  2.5 % 97.5 %
(Intercept) 33.06 33.3
g6pd_202_rtpcrHET -0.3948 0.1485
g6pd_202_rtpcrHOM/HEMI -0.4472 0.1118
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.69 0.1267 258 0 * * *
g6pd_202_rtpcrHET -0.1073 0.1357 -0.7906 0.4296
g6pd_202_rtpcrHOM/HEMI -0.2011 0.1398 -1.438 0.151
age_at_collection_years_2010 0.06514 0.0149 4.373 1.512e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.061 0.04271 0.03662
  2.5 % 97.5 %
(Intercept) 32.44 32.94
g6pd_202_rtpcrHET -0.3741 0.1594
g6pd_202_rtpcrHOM/HEMI -0.4758 0.07363
age_at_collection_years_2010 0.03586 0.09441

g6pd_202_rtpcr _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33 0.1509 218.7 0 * * *
thalHET -0.454 0.123 -3.692 0.0002604 * * *
thalHOM -1.181 0.1443 -8.187 6.074e-15 * * *
age_at_collection_years_2010 0.08667 0.0163 5.317 1.955e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 0.9715 0.2179 0.2107
  2.5 % 97.5 %
(Intercept) 32.7 33.29
thalHET -0.696 -0.2121
thalHOM -1.465 -0.8974
age_at_collection_years_2010 0.05461 0.1187
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.65009 0.0922826 327 33.46854 33.83163
  • HET 33.19605 0.0812437 327 33.03623 33.35588
  • HOM 32.46889 0.1107804 327 32.25096 32.68683
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.63 0.09597 350.4 0 * * *
thalHET -0.4306 0.1279 -3.367 0.0008505 * * *
thalHOM -1.141 0.15 -7.609 2.951e-13 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.011 0.1503 0.1451
  2.5 % 97.5 %
(Intercept) 33.44 33.82
thalHET -0.6822 -0.179
thalHOM -1.436 -0.846
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.63063 0.0959673 328 33.44184 33.81942
  • HET 33.20000 0.0845506 328 33.03367 33.36633
  • HOM 32.48961 0.1152230 328 32.26294 32.71628
thal n
NORM 111
HET 143
HOM 77

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.6 0.1478 220.5 0 * * *
sickleHET -0.1786 0.1607 -1.112 0.2672
age_at_collection_years_2010 0.08062 0.01782 4.523 8.54e-06 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.063 0.06094 0.05522
  2.5 % 97.5 %
(Intercept) 32.31 32.89
sickleHET -0.4948 0.1375
age_at_collection_years_2010 0.04555 0.1157
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.20722 0.0636438 328 33.08201 33.33242
  • HET 33.02859 0.1475196 328 32.73839 33.31880
sickle n
NORM 279
HET 52

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.2 0.06549 507 0 * * *
sickleHET -0.1464 0.1652 -0.886 0.3763
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
331 1.094 0.00238 -0.0006522
  2.5 % 97.5 %
(Intercept) 33.07 33.33
sickleHET -0.4714 0.1786
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.20215 0.0654884 329 33.07332 33.33098
  • HET 33.05577 0.1516928 329 32.75736 33.35418
sickle n
NORM 279
HET 52

g6pd_202_rtpcr _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.29 0.307 108.4 1.275e-80 * * *
thalHET -0.6954 0.2512 -2.769 0.007176 * *
thalHOM -1.638 0.3499 -4.68 1.335e-05 * * *
age_at_collection_years_2010 0.05481 0.03471 1.579 0.1187
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 0.958 0.2421 0.2101
  2.5 % 97.5 %
(Intercept) 32.68 33.9
thalHET -1.196 -0.1946
thalHOM -2.335 -0.9399
age_at_collection_years_2010 -0.01439 0.124
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.68888 0.2008136 71 33.28846 34.08929
  • HET 32.99351 0.1515758 71 32.69127 33.29574
  • HOM 32.05129 0.2824211 71 31.48816 32.61442
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.66 0.2018 166.8 6.391e-95 * * *
thalHET -0.6715 0.2533 -2.651 0.009858 * *
thalHOM -1.515 0.3447 -4.395 3.749e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 0.9679 0.2155 0.1937
  2.5 % 97.5 %
(Intercept) 33.25 34.06
thalHET -1.176 -0.1666
thalHOM -2.202 -0.8277
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.65652 0.2018284 72 33.25418 34.05886
  • HET 32.98500 0.1530440 72 32.67991 33.29009
  • HOM 32.14167 0.2794188 72 31.58466 32.69868
thal n
NORM 23
HET 40
HOM 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.02 0.3236 102 1.257e-79 * * *
sickleHET -0.3406 0.325 -1.048 0.2981
age_at_collection_years_2010 0.01335 0.03869 0.3451 0.731
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 1.083 0.01866 -0.0086
  2.5 % 97.5 %
(Intercept) 32.38 33.67
sickleHET -0.9886 0.3073
age_at_collection_years_2010 -0.06378 0.09048
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.11959 0.1389507 72 32.84259 33.39658
  • HET 32.77894 0.2924287 72 32.19600 33.36189
sickle n
NORM 61
HET 14

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.12 0.1378 240.4 1.41e-107 * * *
sickleHET -0.3587 0.3189 -1.125 0.2644
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
75 1.076 0.01704 0.00357
  2.5 % 97.5 %
(Intercept) 32.85 33.4
sickleHET -0.9942 0.2769
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.12295 0.1377696 73 32.84838 33.39753
  • HET 32.76429 0.2875771 73 32.19115 33.33743
sickle n
NORM 61
HET 14

g6pd_202_rtpcr _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

g6pd_202_rtpcr _ HOM/HEMI ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.15 0.3009 110.1 1.548e-76 * * *
thalHET -0.2784 0.2279 -1.222 0.2261
thalHOM -1.822 0.3211 -5.673 3.372e-07 * * *
age_at_collection_years_2010 0.03424 0.03209 1.067 0.2899
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.842 0.3531 0.3237
  2.5 % 97.5 %
(Intercept) 32.55 33.75
thalHET -0.7334 0.1765
thalHOM -2.463 -1.181
age_at_collection_years_2010 -0.02983 0.09831
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.42285 0.1803283 66 33.06281 33.78288
  • HET 33.14440 0.1374208 66 32.87003 33.41877
  • HOM 31.60102 0.2669850 66 31.06796 32.13407
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.4 0.1797 185.9 1.236e-92 * * *
thalHET -0.244 0.2258 -1.081 0.2837
thalHOM -1.825 0.3214 -5.676 3.215e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 0.8428 0.342 0.3223
  2.5 % 97.5 %
(Intercept) 33.05 33.76
thalHET -0.6947 0.2067
thalHOM -2.466 -1.183
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.40455 0.1796958 67 33.04587 33.76322
  • HET 33.16053 0.1367280 67 32.88762 33.43344
  • HOM 31.58000 0.2665319 67 31.04800 32.11200
thal n
NORM 22
HET 38
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.76 0.3355 97.63 5.718e-74 * * *
sickleHET -0.4894 0.3211 -1.524 0.1321
age_at_collection_years_2010 0.04183 0.03808 1.099 0.2759
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 1.012 0.05099 0.02266
  2.5 % 97.5 %
(Intercept) 32.09 33.43
sickleHET -1.13 0.1514
age_at_collection_years_2010 -0.03417 0.1178
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.09533 0.1329110 67 32.83004 33.36062
  • HET 32.60591 0.2922421 67 32.02260 33.18923
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.1 0.1331 248.6 2.367e-102 * * *
sickleHET -0.4966 0.3215 -1.545 0.1271
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
70 1.014 0.03389 0.01969
  2.5 % 97.5 %
(Intercept) 32.83 33.36
sickleHET -1.138 0.145
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.09655 0.1331085 68 32.83094 33.36217
  • HET 32.60000 0.2926369 68 32.01605 33.18395
sickle n
NORM 58
HET 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.6 0.07902 425.2 0 * * *
thalHET -0.4483 0.1032 -4.343 1.719e-05 * * *
thalHOM -1.247 0.1268 -9.833 6.968e-21 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.987 0.1699 0.1664
  2.5 % 97.5 %
(Intercept) 33.45 33.76
thalHET -0.6511 -0.2455
thalHOM -1.496 -0.9978
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.07 0.1242 266.1 0 * * *
thalHET -0.4787 0.1003 -4.772 2.438e-06 * * *
thalHOM -1.294 0.1234 -10.49 2.893e-23 * * *
age_at_collection_years_2010 0.07389 0.01346 5.491 6.526e-08 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 0.9579 0.2198 0.2148
  2.5 % 97.5 %
(Intercept) 32.82 33.31
thalHET -0.6759 -0.2816
thalHOM -1.536 -1.051
age_at_collection_years_2010 0.04745 0.1003

thal _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.01 0.1952 169.1 6.134e-175 * * *
g6pd_202_rtpcrHET 0.07775 0.2215 0.351 0.7261
g6pd_202_rtpcrHOM/HEMI -0.2433 0.2252 -1.08 0.2817
age_at_collection_years_2010 0.08518 0.02354 3.619 0.0004017 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.9648 0.08539 0.06734
  2.5 % 97.5 %
(Intercept) 32.62 33.39
g6pd_202_rtpcrHET -0.3599 0.5154
g6pd_202_rtpcrHOM/HEMI -0.6882 0.2017
age_at_collection_years_2010 0.03868 0.1317
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.62541 0.0915865 152 33.44446 33.80636
  • HET 33.70316 0.2015878 152 33.30489 34.10144
  • HOM/HEMI 33.38213 0.2057901 152 32.97555 33.78870
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.63 0.09513 353.5 9.716e-225 * * *
g6pd_202_rtpcrHET 0.02589 0.2296 0.1128 0.9104
g6pd_202_rtpcrHOM/HEMI -0.2261 0.2339 -0.9666 0.3353
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 1.002 0.006574 -0.006412
  2.5 % 97.5 %
(Intercept) 33.44 33.82
g6pd_202_rtpcrHET -0.4277 0.4795
g6pd_202_rtpcrHOM/HEMI -0.6882 0.236
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.63063 0.0951271 153 33.44270 33.81856
  • HET 33.65652 0.2089786 153 33.24367 34.06938
  • HOM/HEMI 33.40455 0.2136753 153 32.98241 33.82668
g6pd_202_rtpcr n
NORM 111
HET 23
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.05 0.1904 173.6 1.346e-177 * * *
sickleHET -0.2751 0.2065 -1.333 0.1847
age_at_collection_years_2010 0.08297 0.02338 3.549 0.0005146 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.9607 0.08722 0.07529
  2.5 % 97.5 %
(Intercept) 32.67 33.42
sickleHET -0.683 0.1328
age_at_collection_years_2010 0.03678 0.1292
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.64842 0.0842622 153 33.48195 33.81488
  • HET 33.37330 0.1884572 153 33.00098 33.74561
sickle n
NORM 130
HET 26

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.65 0.08737 385.2 1.057e-231 * * *
sickleHET -0.2938 0.214 -1.373 0.1717
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
156 0.9962 0.01209 0.005678
  2.5 % 97.5 %
(Intercept) 33.48 33.82
sickleHET -0.7166 0.1289
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.65154 0.0873715 154 33.47894 33.82414
  • HET 33.35769 0.1953685 154 32.97174 33.74364
sickle n
NORM 130
HET 26

thal _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.65 0.1736 188 2.789e-242 * * *
g6pd_202_rtpcrHET -0.1826 0.1777 -1.027 0.3054
g6pd_202_rtpcrHOM/HEMI -0.1079 0.1821 -0.5929 0.5539
age_at_collection_years_2010 0.07305 0.02008 3.638 0.0003433 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 0.9923 0.06346 0.05051
  2.5 % 97.5 %
(Intercept) 32.3 32.99
g6pd_202_rtpcrHET -0.5328 0.1677
g6pd_202_rtpcrHOM/HEMI -0.4668 0.2509
age_at_collection_years_2010 0.03347 0.1126
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.20591 0.0829923 217 33.04233 33.36948
  • HET 33.02333 0.1572424 217 32.71341 33.33325
  • HOM/HEMI 33.09796 0.1618810 217 32.77890 33.41702
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.2 0.08527 389.3 7.331e-312 * * *
g6pd_202_rtpcrHET -0.215 0.1824 -1.179 0.2398
g6pd_202_rtpcrHOM/HEMI -0.03947 0.1861 -0.2121 0.8322
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 1.02 0.006341 -0.002775
  2.5 % 97.5 %
(Intercept) 33.03 33.37
g6pd_202_rtpcrHET -0.5745 0.1445
g6pd_202_rtpcrHOM/HEMI -0.4063 0.3273
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.20000 0.0852731 218 33.03193 33.36807
  • HET 32.98500 0.1612316 218 32.66723 33.30277
  • HOM/HEMI 33.16053 0.1654201 218 32.83450 33.48655
g6pd_202_rtpcr n
NORM 143
HET 40
HOM/HEMI 38

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.63 0.1663 196.2 3.697e-247 * * *
sickleHET -0.3634 0.1817 -2 0.04677 *
age_at_collection_years_2010 0.07607 0.0198 3.842 0.0001599 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 0.9837 0.07522 0.06674
  2.5 % 97.5 %
(Intercept) 32.3 32.96
sickleHET -0.7216 -0.005238
age_at_collection_years_2010 0.03705 0.1151
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.21186 0.0721621 218 33.06963 33.35408
  • HET 32.84842 0.1666588 218 32.51995 33.17689
sickle n
NORM 186
HET 35

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.2 0.07436 446.5 0 * * *
sickleHET -0.3123 0.1869 -1.671 0.09606
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
221 1.014 0.0126 0.008088
  2.5 % 97.5 %
(Intercept) 33.06 33.35
sickleHET -0.6806 0.05594
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.20376 0.0743633 219 33.05720 33.35032
  • HET 32.89143 0.1714277 219 32.55357 33.22929
sickle n
NORM 186
HET 35

thal _ HOM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.03 0.2402 133.3 7.496e-110 * * *
g6pd_202_rtpcrHET -0.4165 0.2626 -1.586 0.1161
g6pd_202_rtpcrHOM/HEMI -0.8896 0.2824 -3.15 0.00218 * *
age_at_collection_years_2010 0.05872 0.02829 2.076 0.04064 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.8396 0.1409 0.1138
  2.5 % 97.5 %
(Intercept) 31.56 32.51
g6pd_202_rtpcrHET -0.9379 0.105
g6pd_202_rtpcrHOM/HEMI -1.45 -0.329
age_at_collection_years_2010 0.002555 0.1149
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 32.49589 0.0957237 95 32.30586 32.68593
  • HET 32.07944 0.2442051 95 31.59463 32.56425
  • HOM/HEMI 31.60630 0.2657918 95 31.07864 32.13397
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.49 0.09731 333.9 5.979e-149 * * *
g6pd_202_rtpcrHET -0.3479 0.265 -1.313 0.1923
g6pd_202_rtpcrHOM/HEMI -0.9096 0.287 -3.169 0.002052 * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.8539 0.102 0.08326
  2.5 % 97.5 %
(Intercept) 32.3 32.68
g6pd_202_rtpcrHET -0.874 0.1781
g6pd_202_rtpcrHOM/HEMI -1.479 -0.3399
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 32.48961 0.0973102 96 32.29645 32.68277
  • HET 32.14167 0.2464978 96 31.65237 32.63096
  • HOM/HEMI 31.58000 0.2700248 96 31.04401 32.11600
g6pd_202_rtpcr n
NORM 77
HET 12
HOM/HEMI 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.88 0.2592 123 2.011e-107 * * *
sickleHET 0.0787 0.2374 0.3315 0.741
age_at_collection_years_2010 0.05854 0.02975 1.968 0.05198
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.8834 0.03883 0.0188
  2.5 % 97.5 %
(Intercept) 31.37 32.39
sickleHET -0.3926 0.55
age_at_collection_years_2010 -0.0005115 0.1176
  • sickle emmean SE df lower.CL upper.CL
  • NORM 32.34204 0.0976982 96 32.14811 32.53597
  • HET 32.42074 0.2157666 96 31.99245 32.84904
sickle n
NORM 82
HET 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.35 0.09899 326.8 2.28e-149 * * *
sickleHET 0.01815 0.2389 0.07598 0.9396
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
99 0.8964 5.951e-05 -0.01025
  2.5 % 97.5 %
(Intercept) 32.16 32.55
sickleHET -0.456 0.4923
  • sickle emmean SE df lower.CL upper.CL
  • NORM 32.35244 0.0989890 97 32.15597 32.54890
  • HET 32.37059 0.2174049 97 31.93910 32.80208
sickle n
NORM 82
HET 17

thal _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.17 0.05406 613.7 0 * * *
sickleHET -0.2413 0.1335 -1.807 0.07142
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k])))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.078 0.00684 0.004745
  2.5 % 97.5 %
(Intercept) 33.07 33.28
sickleHET -0.5037 0.02112
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.69 0.1244 262.7 0 * * *
sickleHET -0.2416 0.1311 -1.843 0.06598
age_at_collection_years_2010 0.06428 0.01483 4.334 1.791e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(rbc_polymorphism[k], “+”, age_at_collection_years)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
476 1.059 0.04477 0.04073
  2.5 % 97.5 %
(Intercept) 32.44 32.93
sickleHET -0.4992 0.01602
age_at_collection_years_2010 0.03514 0.09343

sickle _ NORM ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.72 0.1396 234.4 0 * * *
g6pd_202_rtpcrHET -0.08386 0.1529 -0.5484 0.5837
g6pd_202_rtpcrHOM/HEMI -0.1449 0.1564 -0.926 0.355
age_at_collection_years_2010 0.06499 0.01652 3.935 9.825e-05 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 1.082 0.03928 0.03196
  2.5 % 97.5 %
(Intercept) 32.44 32.99
g6pd_202_rtpcrHET -0.3845 0.2168
g6pd_202_rtpcrHOM/HEMI -0.4524 0.1627
age_at_collection_years_2010 0.03252 0.09746
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.20859 0.0647842 394 33.08122 33.33595
  • HET 33.12473 0.1385064 394 32.85242 33.39703
  • HOM/HEMI 33.06373 0.1422872 394 32.78399 33.34347
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.2 0.06594 503.5 0 * * *
g6pd_202_rtpcrHET -0.0792 0.1557 -0.5087 0.6112
g6pd_202_rtpcrHOM/HEMI -0.1056 0.1589 -0.6644 0.5068
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 1.101 0.001517 -0.003539
  2.5 % 97.5 %
(Intercept) 33.07 33.33
g6pd_202_rtpcrHET -0.3853 0.2269
g6pd_202_rtpcrHOM/HEMI -0.4181 0.2069
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.20215 0.0659404 395 33.07251 33.33179
  • HET 33.12295 0.1410225 395 32.84570 33.40020
  • HOM/HEMI 33.09655 0.1446237 395 32.81222 33.38088
g6pd_202_rtpcr n
NORM 279
HET 61
HOM/HEMI 58

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.09 0.1375 240.6 0 * * *
thalHET -0.4682 0.1108 -4.225 2.973e-05 * * *
thalHOM -1.358 0.1371 -9.904 8.476e-21 * * *
age_at_collection_years_2010 0.07631 0.01481 5.152 4.09e-07 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 0.9689 0.2294 0.2235
  2.5 % 97.5 %
(Intercept) 32.82 33.36
thalHET -0.6861 -0.2503
thalHOM -1.628 -1.088
age_at_collection_years_2010 0.04719 0.1054
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.67324 0.0850790 394 33.50597 33.84050
  • HET 33.20500 0.0710407 394 33.06533 33.34466
  • HOM 32.31524 0.1072361 394 32.10442 32.52607
thal n
NORM 130
HET 186
HOM 82

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.65 0.08768 383.8 0 * * *
thalHET -0.4478 0.1143 -3.918 0.0001052 * * *
thalHOM -1.299 0.141 -9.215 1.874e-18 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
398 0.9997 0.1774 0.1733
  2.5 % 97.5 %
(Intercept) 33.48 33.82
thalHET -0.6725 -0.2231
thalHOM -1.576 -1.022
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.65154 0.0876788 395 33.47916 33.82391
  • HET 33.20376 0.0733010 395 33.05965 33.34787
  • HOM 32.35244 0.1103975 395 32.13540 32.56948
thal n
NORM 130
HET 186
HOM 82

sickle _ HET ________________________________________________________________

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.56 0.3007 108.3 2.995e-83 * * *
g6pd_202_rtpcrHET -0.1852 0.2898 -0.6389 0.5249
g6pd_202_rtpcrHOM/HEMI -0.4579 0.3021 -1.516 0.1339
age_at_collection_years_2010 0.06337 0.03433 1.846 0.06893
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.9433 0.07766 0.04027
  2.5 % 97.5 %
(Intercept) 31.96 33.16
g6pd_202_rtpcrHET -0.7626 0.3923
g6pd_202_rtpcrHOM/HEMI -1.06 0.1441
age_at_collection_years_2010 -0.005041 0.1318
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.03701 0.1312090 74 32.77557 33.29845
  • HET 32.85185 0.2565379 74 32.34069 33.36302
  • HOM/HEMI 32.57912 0.2725477 74 32.03606 33.12218
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.06 0.1329 248.7 3.788e-111 * * *
g6pd_202_rtpcrHET -0.2915 0.2886 -1.01 0.3157
g6pd_202_rtpcrHOM/HEMI -0.4558 0.3069 -1.485 0.1417
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 2, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.9583 0.0352 0.00947
  2.5 % 97.5 %
(Intercept) 32.79 33.32
g6pd_202_rtpcrHET -0.8663 0.2833
g6pd_202_rtpcrHOM/HEMI -1.067 0.1556
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 33.05577 0.1328971 75 32.79102 33.32051
  • HET 32.76429 0.2561258 75 32.25406 33.27451
  • HOM/HEMI 32.60000 0.2766474 75 32.04889 33.15111
g6pd_202_rtpcr n
NORM 52
HET 14
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.86 0.2848 115.4 2.761e-85 * * *
thalHET -0.5475 0.2314 -2.366 0.02058 *
thalHOM -0.9845 0.2751 -3.579 0.0006138 * * *
age_at_collection_years_2010 0.06972 0.03198 2.18 0.03243 *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.882 0.1936 0.1609
  2.5 % 97.5 %
(Intercept) 32.3 33.43
thalHET -1.009 -0.08649
thalHOM -1.533 -0.4364
age_at_collection_years_2010 0.005997 0.1334
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.39360 0.1737603 74 33.04737 33.73982
  • HET 32.84607 0.1505332 74 32.54612 33.14601
  • HOM 32.40906 0.2146477 74 31.98137 32.83676
thal n
NORM 26
HET 35
HOM 17

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.36 0.1773 188.2 4.452e-102 * * *
thalHET -0.4663 0.234 -1.993 0.04995 *
thalHOM -0.9871 0.2819 -3.502 0.0007828 * * *
Fitting linear model: as.formula(paste(cbc_array[j], “~”, paste(colnames(b[-c(length(b), 1, 3, 4, 5)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
78 0.9038 0.1419 0.119
  2.5 % 97.5 %
(Intercept) 33 33.71
thalHET -0.9324 -0.0001032
thalHOM -1.549 -0.4255
  • thal emmean SE df lower.CL upper.CL
  • NORM 33.35769 0.1772528 75 33.00459 33.71080
  • HET 32.89143 0.1527727 75 32.58709 33.19577
  • HOM 32.37059 0.2192075 75 31.93390 32.80727
thal n
NORM 26
HET 35
HOM 17

sickle _ HOM ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

sickle _ HOM/HEMI ________________________________________________________________
ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

##Association of g6pd enzyme activity with each polymorphism in all individuals {.tabset}

u_rcc

g6pd_202_rtpcr + malaria +ve

Shapiro-Wilk normality test: b[, ed[j]]
Test statistic P value
0.9798 1.596e-05 * * *
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
Test statistic P value
0.9739 1.105e-05 * * *
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
3.593 2 0.1659
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
Test statistic df P value
0.7703 2 0.6804

Kruskal walis___________________________________________________________________

Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
97.13 2 8.119e-22 * * *
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 175.1 3.364 52.06 8.732e-183
b[, rbc_polymorphism[i]]HET -44.35 7.801 -5.685 2.487e-08
b[, rbc_polymorphism[i]]HOM/HEMI -127 9.629 -13.19 2.244e-33
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 58.47 0.3144 0.3111
  2.5 % 97.5 %
(Intercept) 168.5 181.8
b[, rbc_polymorphism[i]]HET -59.69 -29.02
b[, rbc_polymorphism[i]]HOM/HEMI -146 -108.1
g6pd_202_rtpcr n
NORM 302
HET 69
HOM/HEMI 42
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 198.2 9.147 21.67 1.86e-66
g6pd_202_rtpcrHET -49.97 8.549 -5.844 1.173e-08
g6pd_202_rtpcrHOM/HEMI -136.3 9.851 -13.83 5.75e-35
age_at_collection_years_2010 -1.066 0.9925 -1.074 0.2837
ethnicDigo 66.88 41.15 1.625 0.105
ethnicDurum -11.65 41.11 -0.2834 0.7771
ethnicGiriama -18.7 6.83 -2.738 0.006507
ethnicJibana -14.2 13.15 -1.08 0.2809
ethnicKambe -34.36 41.1 -0.836 0.4037
ethnicKauma -9.943 22.55 -0.441 0.6595
ethnicRabai 18.81 41.41 0.4542 0.65
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 57.52 0.383 0.3653
  2.5 % 97.5 %
(Intercept) 180.2 216.2
g6pd_202_rtpcrHET -66.78 -33.15
g6pd_202_rtpcrHOM/HEMI -155.6 -116.9
age_at_collection_years_2010 -3.018 0.8864
ethnicDigo -14.05 147.8
ethnicDurum -92.49 69.2
ethnicGiriama -32.13 -5.264
ethnicJibana -40.05 11.66
ethnicKambe -115.2 46.48
ethnicKauma -54.29 34.4
ethnicRabai -62.64 100.3
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 189.54356 10.86986 347 168.16447 210.92266
  • HET 139.57741 12.68905 347 114.62027 164.53454
  • HOM/HEMI 53.27477 13.99091 347 25.75712 80.79242
g6pd_202_rtpcr n
NORM 302
HET 69
HOM/HEMI 42

thal + malaria +ve

Shapiro-Wilk normality test: b[, ed[j]]
Test statistic P value
0.9798 1.596e-05 * * *
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
Test statistic P value
0.9739 1.105e-05 * * *
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.5709 2 0.7517
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
Test statistic df P value
0.7703 2 0.6804

Kruskal walis___________________________________________________________________

Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.5567 2 0.757
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 160.6 6.067 26.47 8.978e-91
b[, rbc_polymorphism[i]]HET -9.239 7.926 -1.166 0.2445
b[, rbc_polymorphism[i]]HOM -7.264 9.692 -0.7496 0.454
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 70.49 0.003418 -0.001444
  2.5 % 97.5 %
(Intercept) 148.7 172.6
b[, rbc_polymorphism[i]]HET -24.82 6.342
b[, rbc_polymorphism[i]]HOM -26.32 11.79
thal n
NORM 135
HET 191
HOM 87
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 177.2 12.09 14.65 3.457e-38
thalHET -10.86 8.8 -1.234 0.218
thalHOM -13.41 11 -1.219 0.2235
age_at_collection_years_2010 -1.29 1.251 -1.031 0.3032
ethnicDigo 75.86 51.81 1.464 0.144
ethnicDurum -1.648 51.72 -0.03186 0.9746
ethnicGiriama -7.496 8.512 -0.8806 0.3792
ethnicJibana -0.1585 16.53 -0.00959 0.9924
ethnicKambe 1.937 52.21 0.03709 0.9704
ethnicKauma -22.01 28.3 -0.778 0.4371
ethnicRabai 40.25 52.23 0.7706 0.4414
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 72.35 0.02384 -0.004294
  2.5 % 97.5 %
(Intercept) 153.4 201
thalHET -28.17 6.449
thalHOM -35.03 8.218
age_at_collection_years_2010 -3.751 1.171
ethnicDigo -26.04 177.8
ethnicDurum -103.4 100.1
ethnicGiriama -24.24 9.246
ethnicJibana -32.66 32.35
ethnicKambe -100.8 104.6
ethnicKauma -77.67 33.64
ethnicRabai -62.48 143
  • thal emmean SE df lower.CL upper.CL
  • NORM 178.0154 14.64237 347 149.2165 206.8144
  • HET 167.1570 14.16498 347 139.2970 195.0170
  • HOM 164.6073 15.25043 347 134.6124 194.6022
thal n
NORM 135
HET 191
HOM 87

sickle + malaria +ve

Shapiro-Wilk normality test: b[, ed[j]]
Test statistic P value
0.9798 1.596e-05 * * *
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
Test statistic P value
0.9739 1.105e-05 * * *
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.6778 1 0.4104
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
Test statistic df P value
0.7703 2 0.6804

Kruskal walis___________________________________________________________________

Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.7254 1 0.3944
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 153.9 3.747 41.08 1.309e-147
b[, rbc_polymorphism[i]]HET 6.213 9.913 0.6268 0.5312
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 70.5 0.0009549 -0.001476
  2.5 % 97.5 %
(Intercept) 146.6 161.3
b[, rbc_polymorphism[i]]HET -13.27 25.7
sickle n
NORM 354
HET 59
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 170 11.22 15.15 3.437e-40
sickleHET 4.391 11.4 0.3852 0.7003
age_at_collection_years_2010 -1.423 1.25 -1.139 0.2556
ethnicDigo 71.21 51.88 1.373 0.1707
ethnicDurum -5.523 51.69 -0.1068 0.915
ethnicGiriama -7.602 8.519 -0.8923 0.3728
ethnicJibana -2.012 16.68 -0.1206 0.9041
ethnicKambe -7.581 52.59 -0.1442 0.8855
ethnicKauma -18.79 28.31 -0.6637 0.5073
ethnicRabai 45.49 52.23 0.8709 0.3844
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 72.44 0.0185 -0.00688
  2.5 % 97.5 %
(Intercept) 147.9 192.1
sickleHET -18.03 26.81
age_at_collection_years_2010 -3.881 1.035
ethnicDigo -30.82 173.2
ethnicDurum -107.2 96.14
ethnicGiriama -24.36 9.154
ethnicJibana -34.82 30.8
ethnicKambe -111 95.85
ethnicKauma -74.47 36.89
ethnicRabai -57.24 148.2
  • sickle emmean SE df lower.CL upper.CL
  • NORM 168.3653 13.98098 348 140.8675 195.8632
  • HET 172.7565 15.51322 348 142.2450 203.2679
sickle n
NORM 354
HET 59

####Univariate association of g6pd enzyme activity with age, sex, malaria

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 168 8.605 19.52 1.552e-60 * * *
age_at_collection_years_2010 -1.741 1.041 -1.673 0.09507
Fitting linear model: as.formula(paste(ed[j], “~”, age_at_collection_years))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 70.29 0.006765 0.004348
  2.5 % 97.5 %
(Intercept) 151.1 184.9
age_at_collection_years_2010 -3.787 0.3046
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 161.5 4.856 33.25 1.334e-118 * * *
sexMALE -13.49 6.91 -1.953 0.05153
Fitting linear model: as.formula(paste(ed[j], “~”, “sex”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 70.2 0.009192 0.006781
  2.5 % 97.5 %
(Intercept) 151.9 171
sexMALE -27.07 0.09019
sex n
FEMALE 209
MALE 204
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 160.5 6.608 24.28 4.688e-77 * * *
ethnicDigo 69.77 51.61 1.352 0.1773
ethnicDurum -7.56 51.61 -0.1465 0.8836
ethnicGiriama -9.527 8.358 -1.14 0.2551
ethnicJibana -0.0934 16.48 -0.005669 0.9955
ethnicKambe -5.478 51.61 -0.1061 0.9155
ethnicKauma -16.76 28.15 -0.5956 0.5518
ethnicRabai 54.54 51.61 1.057 0.2914
Fitting linear model: as.formula(paste(ed[j], “~”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 72.39 0.01446 -0.005252
  2.5 % 97.5 %
(Intercept) 147.5 173.5
ethnicDigo -31.73 171.3
ethnicDurum -109.1 93.94
ethnicGiriama -25.97 6.912
ethnicJibana -32.5 32.31
ethnicKambe -107 96.03
ethnicKauma -72.12 38.59
ethnicRabai -46.97 156
ethnic n
Chonyi 120
Digo 2
Durum 2
Giriama 200
Jibana 23
Kambe 2
Kauma 7
Rabai 2
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 152.8 3.886 39.33 3.061e-141 * * *
malaria_statusassymptomatic_malaria 6.727 10.43 0.6448 0.5194
malaria_statusuncomplicated_malaria 14.83 13.24 1.12 0.2635
Fitting linear model: as.formula(paste(ed[j], “~”, malariapositive))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 70.48 0.003718 -0.001142
  2.5 % 97.5 %
(Intercept) 145.2 160.5
malaria_statusassymptomatic_malaria -13.78 27.23
malaria_statusuncomplicated_malaria -11.2 40.86
malaria_status n
no_malaria 329
assymptomatic_malaria 53
uncomplicated_malaria 31

####Association of g6pd enzyme activity with each polymorphism and age (and interactions between them)

Shapiro-Wilk normality test: pgd_genopheno_01042018[, ed[j]]
Test statistic P value
0.9798 1.596e-05 * * *
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
Test statistic df P value
4.872 1 0.0273 *
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", "g6pd_202_rtpcr"]
Test statistic df P value
7.006 1 0.008123 * *
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
Test statistic df P value
3.241 1 0.07181
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "sickle"]
Test statistic df P value
1.417 1 0.234

u_ghb3

g6pd_202_rtpcr + malaria +ve

Shapiro-Wilk normality test: b[, ed[j]]
Test statistic P value
0.9864 0.0006674 * * *
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
Test statistic P value
0.9845 0.001283 * *
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
6.705 2 0.03499 *
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
Test statistic df P value
1.171 2 0.5569

Kruskal walis___________________________________________________________________

Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
108.9 2 2.273e-24 * * *
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.438 0.1478 50.32 1.516e-177
b[, rbc_polymorphism[i]]HET -2.168 0.3428 -6.325 6.619e-10
b[, rbc_polymorphism[i]]HOM/HEMI -5.627 0.423 -13.3 8.25e-34
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 2.569 0.3235 0.3202
  2.5 % 97.5 %
(Intercept) 7.147 7.728
b[, rbc_polymorphism[i]]HET -2.842 -1.494
b[, rbc_polymorphism[i]]HOM/HEMI -6.458 -4.795
g6pd_202_rtpcr n
NORM 302
HET 69
HOM/HEMI 42
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.814 0.3905 22.57 4.679e-70
g6pd_202_rtpcrHET -2.355 0.365 -6.451 3.73e-10
g6pd_202_rtpcrHOM/HEMI -5.902 0.4205 -14.04 9.363e-36
age_at_collection_years_2010 -0.1215 0.04237 -2.869 0.004374
ethnicDigo 2.866 1.757 1.631 0.1037
ethnicDurum 0.1389 1.755 0.07918 0.9369
ethnicGiriama -0.6708 0.2916 -2.301 0.022
ethnicJibana -0.3902 0.5612 -0.6953 0.4873
ethnicKambe -0.6613 1.755 -0.3769 0.7065
ethnicKauma 0.4366 0.9626 0.4536 0.6504
ethnicRabai 0.1766 1.768 0.09988 0.9205
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 2.456 0.4022 0.385
  2.5 % 97.5 %
(Intercept) 8.046 9.582
g6pd_202_rtpcrHET -3.072 -1.637
g6pd_202_rtpcrHOM/HEMI -6.729 -5.075
age_at_collection_years_2010 -0.2049 -0.03821
ethnicDigo -0.5893 6.321
ethnicDurum -3.312 3.59
ethnicGiriama -1.244 -0.09736
ethnicJibana -1.494 0.7136
ethnicKambe -4.112 2.79
ethnicKauma -1.457 2.33
ethnicRabai -3.3 3.654
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 8.106799 0.4640401 347 7.194113 9.019484
  • HET 5.752241 0.5417026 347 4.686807 6.817674
  • HOM/HEMI 2.204419 0.5972794 347 1.029675 3.379162
g6pd_202_rtpcr n
NORM 302
HET 69
HOM/HEMI 42

thal + malaria +ve

Shapiro-Wilk normality test: b[, ed[j]]
Test statistic P value
0.9864 0.0006674 * * *
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
Test statistic P value
0.9845 0.001283 * *
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
2.53 2 0.2823
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
Test statistic df P value
1.171 2 0.5569

Kruskal walis___________________________________________________________________

Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
8.596 2 0.0136 *
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.224 0.2663 23.37 2.14e-77
b[, rbc_polymorphism[i]]HET 0.1086 0.3479 0.312 0.7552
b[, rbc_polymorphism[i]]HOM 1.089 0.4254 2.56 0.01083
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 3.094 0.01829 0.0135
  2.5 % 97.5 %
(Intercept) 5.7 6.747
b[, rbc_polymorphism[i]]HET -0.5754 0.7925
b[, rbc_polymorphism[i]]HOM 0.2527 1.925
thal n
NORM 135
HET 191
HOM 87
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.439 0.5197 14.31 7.704e-37
thalHET 0.06637 0.3783 0.1755 0.8608
thalHOM 0.8384 0.4726 1.774 0.07696
age_at_collection_years_2010 -0.1425 0.05379 -2.649 0.008452
ethnicDigo 3.191 2.227 1.433 0.1528
ethnicDurum 0.05463 2.223 0.02457 0.9804
ethnicGiriama -0.2151 0.3659 -0.588 0.5569
ethnicJibana 0.2012 0.7104 0.2832 0.7772
ethnicKambe 0.04932 2.244 0.02198 0.9825
ethnicKauma 0.2075 1.216 0.1706 0.8646
ethnicRabai 1.591 2.245 0.7087 0.479
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 3.11 0.04113 0.0135
  2.5 % 97.5 %
(Intercept) 6.417 8.461
thalHET -0.6776 0.8103
thalHOM -0.09118 1.768
age_at_collection_years_2010 -0.2482 -0.03667
ethnicDigo -1.189 7.571
ethnicDurum -4.318 4.428
ethnicGiriama -0.9348 0.5045
ethnicJibana -1.196 1.598
ethnicKambe -4.365 4.463
ethnicKauma -2.185 2.6
ethnicRabai -2.825 6.007
  • thal emmean SE df lower.CL upper.CL
  • NORM 6.967416 0.6294013 347 5.729495 8.205338
  • HET 7.033784 0.6088811 347 5.836222 8.231346
  • HOM 7.805847 0.6555390 347 6.516517 9.095177
thal n
NORM 135
HET 191
HOM 87

sickle + malaria +ve

Shapiro-Wilk normality test: b[, ed[j]]
Test statistic P value
0.9864 0.0006674 * * *
Shapiro-Wilk normality test: b[b[, malariapositive] == "no_malaria", ed[j]]
Test statistic P value
0.9845 0.001283 * *
Fligner-Killeen test of homogeneity of variances: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.4735 1 0.4914
Kruskal-Wallis rank sum test: b[, ed[j]] by b[, malariapositive]
Test statistic df P value
1.171 2 0.5569

Kruskal walis___________________________________________________________________

Kruskal-Wallis rank sum test: b[, ed[j]] by b[, rbc_polymorphism[i]]
Test statistic df P value
0.8716 1 0.3505
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.457 0.1657 38.97 4.152e-140
b[, rbc_polymorphism[i]]HET 0.3275 0.4383 0.7471 0.4555
Fitting linear model: b[, ed[j]] ~ b[, rbc_polymorphism[i]]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 3.117 0.001356 -0.001074
  2.5 % 97.5 %
(Intercept) 6.131 6.782
b[, rbc_polymorphism[i]]HET -0.5342 1.189
sickle n
NORM 354
HET 59
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.541 0.4833 15.6 5.522e-42
sickleHET 0.2553 0.4911 0.5199 0.6035
age_at_collection_years_2010 -0.1369 0.05383 -2.544 0.01139
ethnicDigo 2.976 2.235 1.332 0.1839
ethnicDurum 0.3595 2.227 0.1614 0.8719
ethnicGiriama -0.1699 0.367 -0.4628 0.6438
ethnicJibana 0.124 0.7187 0.1726 0.8631
ethnicKambe 0.4839 2.265 0.2136 0.831
ethnicKauma 0.1041 1.22 0.08537 0.932
ethnicRabai 1.351 2.25 0.6002 0.5488
Fitting linear model: as.formula(paste(ed[j], “~”, rbc_polymorphism[i], “+”, age_at_collection_years, “+”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 3.121 0.03163 0.00659
  2.5 % 97.5 %
(Intercept) 6.591 8.492
sickleHET -0.7106 1.221
age_at_collection_years_2010 -0.2428 -0.03107
ethnicDigo -1.42 7.371
ethnicDurum -4.02 4.739
ethnicGiriama -0.8917 0.552
ethnicJibana -1.289 1.537
ethnicKambe -3.972 4.94
ethnicKauma -2.295 2.503
ethnicRabai -3.075 5.776
  • sickle emmean SE df lower.CL upper.CL
  • NORM 7.131412 0.6022981 348 5.946809 8.316014
  • HET 7.386741 0.6683065 348 6.072313 8.701169
sickle n
NORM 354
HET 59

####Univariate association of g6pd enzyme activity with age, sex, malaria

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.746 0.376 20.6 2.633e-65 * * *
age_at_collection_years_2010 -0.1642 0.04547 -3.611 0.0003428 * * *
Fitting linear model: as.formula(paste(ed[j], “~”, age_at_collection_years))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 3.071 0.03075 0.02839
  2.5 % 97.5 %
(Intercept) 7.007 8.485
age_at_collection_years_2010 -0.2536 -0.0748
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.788 0.2148 31.59 4.987e-112 * * *
sexMALE -0.5757 0.3057 -1.883 0.06035
Fitting linear model: as.formula(paste(ed[j], “~”, “sex”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 3.106 0.008557 0.006145
  2.5 % 97.5 %
(Intercept) 6.365 7.21
sexMALE -1.177 0.02517
sex n
FEMALE 209
MALE 204
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.598 0.2868 23.01 5.395e-72 * * *
ethnicDigo 2.777 2.24 1.24 0.2159
ethnicDurum 0.1872 2.24 0.08356 0.9335
ethnicGiriama -0.3499 0.3628 -0.9643 0.3355
ethnicJibana 0.2742 0.7152 0.3834 0.7016
ethnicKambe 0.5427 2.24 0.2423 0.8087
ethnicKauma 0.3226 1.222 0.264 0.7919
ethnicRabai 2.161 2.24 0.9649 0.3353
Fitting linear model: as.formula(paste(ed[j], “~”, “ethnic”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 3.142 0.01296 -0.006781
  2.5 % 97.5 %
(Intercept) 6.034 7.162
ethnicDigo -1.628 7.183
ethnicDurum -4.218 4.593
ethnicGiriama -1.063 0.3637
ethnicJibana -1.132 1.681
ethnicKambe -3.863 4.948
ethnicKauma -2.08 2.725
ethnicRabai -2.244 6.567
ethnic n
Chonyi 120
Digo 2
Durum 2
Giriama 200
Jibana 23
Kambe 2
Kauma 7
Rabai 2
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.411 0.1719 37.3 7.661e-134 * * *
malaria_statusassymptomatic_malaria 0.4357 0.4614 0.9442 0.3456
malaria_statusuncomplicated_malaria 0.489 0.5857 0.8349 0.4042
Fitting linear model: as.formula(paste(ed[j], “~”, malariapositive))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 3.118 0.003484 -0.001377
  2.5 % 97.5 %
(Intercept) 6.073 6.749
malaria_statusassymptomatic_malaria -0.4714 1.343
malaria_statusuncomplicated_malaria -0.6624 1.64
malaria_status n
no_malaria 329
assymptomatic_malaria 53
uncomplicated_malaria 31

####Association of g6pd enzyme activity with each polymorphism and age (and interactions between them)

Shapiro-Wilk normality test: pgd_genopheno_01042018[, ed[j]]
Test statistic P value
0.9864 0.0006674 * * *
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
Test statistic df P value
3.881 1 0.04883 *
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "sickle"] == "HET" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] != "HOM/HEMI", "g6pd_202_rtpcr"]
Test statistic df P value
7.424 1 0.006435 * *
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "NORM", "sickle"]
Test statistic df P value
2.725 1 0.09879
Kruskal-Wallis rank sum test: pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", ed[j]] by pgd_genopheno_01042018[pgd_genopheno_01042018[, "thal"] == "NORM" & pgd_genopheno_01042018[, "g6pd_202_rtpcr"] == "HET", "sickle"]
Test statistic df P value
1.417 1 0.234
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.75 1.611 9.773 4.538e-20 * * *
g6pd_202_rtpcrHET -2.07 0.3575 -5.792 1.569e-08 * * *
g6pd_202_rtpcrHOM/HEMI -5.389 0.4233 -12.73 1.079e-30 * * *
thalHET -0.1266 0.306 -0.4138 0.6793
thalHOM -0.3213 0.4511 -0.7122 0.4768
age_at_collection_years_2010 -0.05983 0.04366 -1.37 0.1714
ethnicDigo 2.661 1.702 1.563 0.1189
ethnicDurum -0.4603 1.7 -0.2707 0.7868
ethnicGiriama -0.7218 0.2819 -2.56 0.01089 *
ethnicJibana -0.4876 0.5436 -0.8969 0.3704
ethnicKambe -1.082 1.714 -0.631 0.5285
ethnicKauma -0.2913 0.9523 -0.3059 0.7599
ethnicRabai 0.1789 1.716 0.1043 0.917
mcv_2010 -0.09934 0.02149 -4.622 5.372e-06 * * *
Fitting linear model: as.formula(paste(“u_ghb3”, “~”, “g6pd_202_rtpcr”, “+”, “thal”, “+”, age_at_collection_years, “+”, “ethnic”, “+”, “mcv_2010”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
358 2.371 0.4473 0.4264
  2.5 % 97.5 %
(Intercept) 12.58 18.92
g6pd_202_rtpcrHET -2.774 -1.367
g6pd_202_rtpcrHOM/HEMI -6.222 -4.557
thalHET -0.7286 0.4753
thalHOM -1.209 0.566
age_at_collection_years_2010 -0.1457 0.02604
ethnicDigo -0.6867 6.009
ethnicDurum -3.804 2.884
ethnicGiriama -1.276 -0.1673
ethnicJibana -1.557 0.5816
ethnicKambe -4.454 2.29
ethnicKauma -2.164 1.582
ethnicRabai -3.196 3.554
mcv_2010 -0.1416 -0.05707
  • g6pd_202_rtpcr thal emmean SE df lower.CL upper.CL
  • NORM NORM 7.921991 0.4877363 344 6.9626706 8.881312
  • HET NORM 5.851545 0.5658453 344 4.7385932 6.964497
  • HOM/HEMI NORM 2.532716 0.6342709 344 1.2851790 3.780254
  • NORM HET 7.795349 0.4757823 344 6.8595404 8.731158
  • HET HET 5.724903 0.5397260 344 4.6633244 6.786481
  • HOM/HEMI HET 2.406074 0.5945318 344 1.2366988 3.575449
  • NORM HOM 7.600664 0.5530672 344 6.5128454 8.688484
  • HET HOM 5.530218 0.6030670 344 4.3440555 6.716381
  • HOM/HEMI HOM 2.211390 0.6319130 344 0.9684899 3.454289

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 237.9 29.32 8.114 5.828e-15 * * *
g6pd_202_rtpcrHET -50.45 7.611 -6.629 1.075e-10 * * *
g6pd_202_rtpcrHOM/HEMI -135.2 9.499 -14.23 1.329e-37 * * *
hgb_2010 -16.72 2.707 -6.177 1.583e-09 * * *
mcv_2010 1.731 0.4217 4.104 4.914e-05 * * *
Fitting linear model: as.formula(paste(“u_rcc”, “~”, “g6pd_202_rtpcr”, “+”, “hgb_2010”, “+”, “mcv_2010”))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
413 56 0.3742 0.3681
  2.5 % 97.5 %
(Intercept) 180.2 295.5
g6pd_202_rtpcrHET -65.41 -35.49
g6pd_202_rtpcrHOM/HEMI -153.9 -116.5
hgb_2010 -22.04 -11.4
mcv_2010 0.9016 2.56
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 176.99634 3.256717 408 170.59431 183.3984
  • HET 126.54807 6.811599 408 113.15786 139.9383
  • HOM/HEMI 41.81346 8.827338 408 24.46072 59.1662

##wambua g6pd activity {.tabset}

g6pd_202_rtpcr

u_rcc

g6pd_202_rtpcr _ NORM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 304.2 42.23 7.202 7.024e-12 * * *
thalHET -4.704 7.776 -0.605 0.5458
thalHOM -3.143 10.88 -0.2889 0.7729
mcv_2010 1.513 0.5639 2.683 0.007781 * *
hgb_2010 -19.14 3.072 -6.233 1.947e-09 * * *
ethnicDigo 27.33 51.57 0.53 0.5966
ethnicDurum 4.01 51.63 0.07766 0.9382
ethnicGiriama -27.99 7.127 -3.927 0.0001112 * * *
ethnicJibana -13.08 13.79 -0.9489 0.3436
ethnicKambe -16.53 37.26 -0.4436 0.6577
ethnicKauma -14.79 24.27 -0.6091 0.543
ethnicRabai 11.11 37.02 0.3002 0.7643
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
261 51.1 0.1902 0.1544
  2.5 % 97.5 %
(Intercept) 221 387.3
thalHET -20.02 10.61
thalHOM -24.57 18.29
mcv_2010 0.4025 2.624
hgb_2010 -25.19 -13.09
ethnicDigo -74.25 128.9
ethnicDurum -97.69 105.7
ethnicGiriama -42.03 -13.95
ethnicJibana -40.24 14.07
ethnicKambe -89.92 56.86
ethnicKauma -62.6 33.02
ethnicRabai -61.81 84.03
  • thal emmean SE df lower.CL upper.CL
  • NORM 194.5230 12.51437 249 169.8755 219.1705
  • HET 189.8186 12.12818 249 165.9317 213.7055
  • HOM 191.3797 14.05533 249 163.6972 219.0622
thal n
NORM 100
HET 133
HOM 69

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 292.4 34.91 8.374 4.009e-15 * * *
sickleHET 22.37 9.497 2.355 0.0193 *
mcv_2010 1.603 0.463 3.463 0.0006291 * * *
hgb_2010 -19.23 3.012 -6.384 8.324e-10 * * *
ethnicDigo 6.667 51.45 0.1296 0.897
ethnicDurum 5.687 50.91 0.1117 0.9111
ethnicGiriama -27.01 7.048 -3.832 0.0001606 * * *
ethnicJibana -13.72 13.61 -1.009 0.3142
ethnicKambe -35.28 37.27 -0.9464 0.3448
ethnicKauma -9.945 23.67 -0.4201 0.6748
ethnicRabai 6.271 36.33 0.1726 0.8631
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
261 50.48 0.2066 0.1748
  2.5 % 97.5 %
(Intercept) 223.6 361.1
sickleHET 3.66 41.07
mcv_2010 0.6913 2.515
hgb_2010 -25.16 -13.3
ethnicDigo -94.67 108
ethnicDurum -94.57 105.9
ethnicGiriama -40.89 -13.13
ethnicJibana -40.52 13.08
ethnicKambe -108.7 38.13
ethnicKauma -56.57 36.68
ethnicRabai -65.28 77.82
  • sickle emmean SE df lower.CL upper.CL
  • NORM 183.6327 11.98967 250 160.0191 207.2463
  • HET 205.9982 12.94117 250 180.5106 231.4858
sickle n
NORM 260
HET 42

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

g6pd_202_rtpcr _ NORM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.54 1.747 10.61 6.047e-22 * * *
thalHET -0.3688 0.342 -1.078 0.282
thalHOM -0.4045 0.4859 -0.8326 0.4059
age_at_collection_years_2010 -0.1202 0.04886 -2.46 0.01458 *
mcv_2010 -0.128 0.02306 -5.551 7.258e-08 * * *
ethnicDigo 1.201 2.269 0.5296 0.5969
ethnicDurum 0.7978 2.256 0.3537 0.7239
ethnicGiriama -0.9314 0.3182 -2.927 0.003741 * *
ethnicJibana -0.2834 0.6015 -0.4712 0.6379
ethnicKambe -1.355 1.625 -0.8339 0.4051
ethnicKauma 0.03406 1.059 0.03215 0.9744
ethnicRabai -0.4652 1.631 -0.2853 0.7757
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
261 2.235 0.244 0.2106
  2.5 % 97.5 %
(Intercept) 15.1 21.98
thalHET -1.042 0.3049
thalHOM -1.361 0.5524
age_at_collection_years_2010 -0.2164 -0.02396
mcv_2010 -0.1734 -0.08258
ethnicDigo -3.267 5.669
ethnicDurum -3.645 5.24
ethnicGiriama -1.558 -0.3046
ethnicJibana -1.468 0.9012
ethnicKambe -4.556 1.846
ethnicKauma -2.052 2.12
ethnicRabai -3.677 2.746
  • thal emmean SE df lower.CL upper.CL
  • NORM 8.163421 0.5510722 249 7.078064 9.248778
  • HET 7.794640 0.5310683 249 6.748682 8.840599
  • HOM 7.758901 0.6155529 249 6.546546 8.971255
thal n
NORM 100
HET 133
HOM 69

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.34 1.318 13.16 2.127e-30 * * *
sickleHET 1.059 0.4159 2.546 0.01151 *
age_at_collection_years_2010 -0.1353 0.04698 -2.88 0.004319 * *
mcv_2010 -0.1158 0.0182 -6.36 9.517e-10 * * *
ethnicDigo 0.2809 2.26 0.1243 0.9012
ethnicDurum 0.8707 2.223 0.3916 0.6957
ethnicGiriama -0.87 0.3151 -2.761 0.00619 * *
ethnicJibana -0.308 0.5934 -0.519 0.6042
ethnicKambe -2.321 1.624 -1.429 0.1543
ethnicKauma 0.3417 1.034 0.3306 0.7412
ethnicRabai -0.6372 1.606 -0.3968 0.6919
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
261 2.207 0.2594 0.2298
  2.5 % 97.5 %
(Intercept) 14.75 19.94
sickleHET 0.2396 1.878
age_at_collection_years_2010 -0.2279 -0.04279
mcv_2010 -0.1516 -0.07991
ethnicDigo -4.17 4.732
ethnicDurum -3.508 5.25
ethnicGiriama -1.491 -0.2494
ethnicJibana -1.477 0.8608
ethnicKambe -5.52 0.8783
ethnicKauma -1.694 2.377
ethnicRabai -3.8 2.526
  • sickle emmean SE df lower.CL upper.CL
  • NORM 7.534012 0.5257781 250 6.498493 8.569531
  • HET 8.592614 0.5662281 250 7.477428 9.707799
sickle n
NORM 260
HET 42

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

g6pd_202_rtpcr _ HET ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 160.6 119.6 1.343 0.1856
thalHET 12.13 18.91 0.6411 0.5245
thalHOM 0.7476 30.88 0.02421 0.9808
mcv_2010 2.572 1.699 1.514 0.1366
hgb_2010 -18.81 9.137 -2.059 0.04499 *
ethnicDigo 59.89 64.68 0.9259 0.3591
ethnicDurum -60.71 67.69 -0.897 0.3742
ethnicGiriama -30.92 17.99 -1.719 0.0921
ethnicJibana -74.69 31.19 -2.395 0.02058 *
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
57 60.97 0.2385 0.1115
  2.5 % 97.5 %
(Intercept) -79.83 401.1
thalHET -25.9 50.15
thalHOM -61.35 62.85
mcv_2010 -0.8443 5.989
hgb_2010 -37.18 -0.4377
ethnicDigo -70.16 189.9
ethnicDurum -196.8 75.38
ethnicGiriama -67.09 5.251
ethnicJibana -137.4 -11.98
  • thal emmean SE df lower.CL upper.CL
  • NORM 123.7734 24.96203 48 73.58389 173.9629
  • HET 135.8987 21.34854 48 92.97456 178.8228
  • HOM 124.5210 27.83654 48 68.55193 180.4902
thal n
NORM 23
HET 36
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 207.2 108.1 1.916 0.06127
sickleHET -41.06 26.52 -1.548 0.128
mcv_2010 2.603 1.438 1.811 0.07631
hgb_2010 -22.16 8.845 -2.505 0.01562 *
ethnicDigo 55.25 62.59 0.8828 0.3816
ethnicDurum -77.08 62.8 -1.227 0.2256
ethnicGiriama -32.33 16.81 -1.923 0.0603
ethnicJibana -46.02 34.24 -1.344 0.185
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
57 59.23 0.2664 0.1616
  2.5 % 97.5 %
(Intercept) -10.17 424.5
sickleHET -94.36 12.24
mcv_2010 -0.2858 5.492
hgb_2010 -39.93 -4.383
ethnicDigo -70.52 181
ethnicDurum -203.3 49.13
ethnicGiriama -66.11 1.456
ethnicJibana -114.8 22.78
  • sickle emmean SE df lower.CL upper.CL
  • NORM 136.45405 19.20863 49 97.85285 175.0552
  • HET 95.39682 29.41710 49 36.28094 154.5127
sickle n
NORM 58
HET 11

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

g6pd_202_rtpcr _ HET ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.882 4.831 2.045 0.04631 *
thalHET 0.4671 0.8063 0.5793 0.5651
thalHOM -0.5208 1.371 -0.38 0.7057
age_at_collection_years_2010 0.02927 0.124 0.236 0.8144
mcv_2010 -0.05808 0.0644 -0.9019 0.3716
ethnicDigo 3.765 2.61 1.443 0.1556
ethnicDurum -1.449 2.746 -0.5276 0.6002
ethnicGiriama -1.05 0.7491 -1.401 0.1675
ethnicJibana -2.262 1.286 -1.76 0.08482
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
57 2.531 0.1628 0.02323
  2.5 % 97.5 %
(Intercept) 0.1684 19.6
thalHET -1.154 2.088
thalHOM -3.277 2.235
age_at_collection_years_2010 -0.22 0.2786
mcv_2010 -0.1876 0.0714
ethnicDigo -1.483 9.012
ethnicDurum -6.969 4.072
ethnicGiriama -2.556 0.4564
ethnicJibana -4.847 0.3225
  • thal emmean SE df lower.CL upper.CL
  • NORM 5.505709 1.0180115 48 3.458860 7.552558
  • HET 5.972799 0.8372109 48 4.289473 7.656124
  • HOM 4.984898 1.1990834 48 2.573979 7.395817
thal n
NORM 23
HET 36
HOM 10

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.805 4.097 2.393 0.02057 *
sickleHET -1.164 1.11 -1.048 0.2997
age_at_collection_years_2010 -0.004102 0.1137 -0.03607 0.9714
mcv_2010 -0.04935 0.0539 -0.9155 0.3644
ethnicDigo 3.869 2.556 1.514 0.1365
ethnicDurum -2.147 2.627 -0.8172 0.4178
ethnicGiriama -1.172 0.7097 -1.651 0.1052
ethnicJibana -1.359 1.44 -0.9434 0.3501
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
57 2.501 0.165 0.04567
  2.5 % 97.5 %
(Intercept) 1.572 18.04
sickleHET -3.394 1.067
age_at_collection_years_2010 -0.2326 0.2244
mcv_2010 -0.1577 0.05897
ethnicDigo -1.267 9.005
ethnicDurum -7.427 3.133
ethnicGiriama -2.598 0.2545
ethnicJibana -4.254 1.536
  • sickle emmean SE df lower.CL upper.CL
  • NORM 5.872907 0.7893974 49 4.286553 7.459260
  • HET 4.709319 1.1779876 49 2.342065 7.076574
sickle n
NORM 58
HET 11

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

g6pd_202_rtpcr _ HOM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
g6pd_202_rtpcr _ HOM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

u_rcc

g6pd_202_rtpcr _ HOM/HEMI ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) -254.3 167.3 -1.52 0.1383
thalHET 5.498 24.84 0.2213 0.8262
thalHOM 36.07 40.2 0.8971 0.3764
mcv_2010 2.663 2.187 1.218 0.2322
hgb_2010 5.802 11.81 0.4914 0.6265
ethnicGiriama 27.22 22.52 1.208 0.2358
ethnicJibana -7.843 67.23 -0.1167 0.9079
ethnicKauma 16.05 54.58 0.2941 0.7706
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
40 64.39 0.1695 -0.01212
  2.5 % 97.5 %
(Intercept) -595.1 86.46
thalHET -45.09 56.09
thalHOM -45.83 118
mcv_2010 -1.791 7.116
hgb_2010 -18.25 29.85
ethnicGiriama -18.66 73.1
ethnicJibana -144.8 129.1
ethnicKauma -95.12 127.2
  • thal emmean SE df lower.CL upper.CL
  • NORM 27.85519 27.05220 32 -27.24833 82.95872
  • HET 33.35293 22.10092 32 -11.66517 78.37102
  • HOM 63.92354 39.64434 32 -16.82935 144.67642
thal n
NORM 12
HET 22
HOM 8

  Estimate Std. Error t value Pr(>|t|)
(Intercept) -149.9 124.3 -1.206 0.2364
sickleHET -0.4567 31.71 -0.0144 0.9886
mcv_2010 1.374 1.722 0.798 0.4306
hgb_2010 6.336 11.82 0.5362 0.5954
ethnicGiriama 29.21 21.97 1.329 0.1929
ethnicJibana -8.41 72.52 -0.116 0.9084
ethnicKauma -3.229 50.7 -0.0637 0.9496
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
40 64.25 0.1475 -0.007544
  2.5 % 97.5 %
(Intercept) -402.7 102.9
sickleHET -64.96 64.05
mcv_2010 -2.13 4.879
hgb_2010 -17.71 30.38
ethnicGiriama -15.5 73.91
ethnicJibana -156 139.1
ethnicKauma -106.4 99.91
  • sickle emmean SE df lower.CL upper.CL
  • NORM 33.54212 22.66274 33 -12.56557 79.64981
  • HET 33.08547 30.43532 33 -28.83565 95.00660
sickle n
NORM 36
HET 6

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

g6pd_202_rtpcr _ HOM/HEMI ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.671 6.045 -0.7727 0.4454
thalHET -0.1367 0.9593 -0.1425 0.8876
thalHOM 0.933 1.557 0.5991 0.5533
age_at_collection_years_2010 0.1554 0.1313 1.184 0.2452
mcv_2010 0.05994 0.07711 0.7774 0.4427
ethnicGiriama 0.5956 0.8666 0.6872 0.4969
ethnicJibana -0.435 2.473 -0.1759 0.8615
ethnicKauma -0.09223 2.041 -0.04519 0.9642
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
40 2.369 0.1549 -0.02993
  2.5 % 97.5 %
(Intercept) -16.98 7.642
thalHET -2.091 1.817
thalHOM -2.239 4.105
age_at_collection_years_2010 -0.112 0.4229
mcv_2010 -0.09712 0.217
ethnicGiriama -1.17 2.361
ethnicJibana -5.472 4.602
ethnicKauma -4.249 4.065
  • thal emmean SE df lower.CL upper.CL
  • NORM 1.246420 1.0081318 32 -0.8070776 3.299917
  • HET 1.109717 0.8151458 32 -0.5506808 2.770115
  • HOM 2.179423 1.4887439 32 -0.8530494 5.211895
thal n
NORM 12
HET 22
HOM 8

  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.833 4.073 -0.4501 0.6556
sickleHET 0.3486 1.152 0.3025 0.7641
age_at_collection_years_2010 0.1684 0.1214 1.387 0.1747
mcv_2010 0.02303 0.05421 0.4249 0.6736
ethnicGiriama 0.662 0.8338 0.794 0.4329
ethnicJibana -0.8899 2.666 -0.3337 0.7407
ethnicKauma -0.5671 1.838 -0.3085 0.7596
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
40 2.352 0.1406 -0.01561
  2.5 % 97.5 %
(Intercept) -10.12 6.453
sickleHET -1.996 2.693
age_at_collection_years_2010 -0.07856 0.4153
mcv_2010 -0.08725 0.1333
ethnicGiriama -1.034 2.358
ethnicJibana -6.315 4.535
ethnicKauma -4.306 3.172
  • sickle emmean SE df lower.CL upper.CL
  • NORM 1.095040 0.829702 33 -0.5930011 2.783082
  • HET 1.443641 1.097995 33 -0.7902468 3.677528
sickle n
NORM 36
HET 6

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

thal

u_rcc

thal _ NORM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 233 66.76 3.49 0.000697 * * *
g6pd_202_rtpcrHET -61.1 16.27 -3.755 0.0002784 * * *
g6pd_202_rtpcrHOM/HEMI -144.1 19.96 -7.223 7.035e-11 * * *
mcv_2010 2.657 0.8514 3.121 0.0023 * *
hgb_2010 -20.83 5.364 -3.883 0.0001762 * * *
ethnicGiriama -25.44 12.43 -2.046 0.04311 *
ethnicJibana -14.04 27.12 -0.5178 0.6056
ethnicKauma -8.184 34.57 -0.2367 0.8133
ethnicRabai 19.37 44.83 0.432 0.6666
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
119 61.16 0.4106 0.3678
  2.5 % 97.5 %
(Intercept) 100.7 365.2
g6pd_202_rtpcrHET -93.35 -28.86
g6pd_202_rtpcrHOM/HEMI -183.7 -104.6
mcv_2010 0.9702 4.345
hgb_2010 -31.46 -10.2
ethnicGiriama -50.07 -0.8018
ethnicJibana -67.78 39.7
ethnicKauma -76.7 60.33
ethnicRabai -69.47 108.2
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 194.98316 12.76436 110 169.687189 220.27913
  • HET 133.87935 19.15047 110 95.927622 171.83108
  • HOM/HEMI 50.84587 21.69640 110 7.848689 93.84304
g6pd_202_rtpcr n
NORM 100
HET 23
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 247.8 82.93 2.989 0.00345 * *
sickleHET 5.45 19.23 0.2834 0.7774
mcv_2010 1.641 1.046 1.569 0.1196
hgb_2010 -17.63 6.623 -2.662 0.008917 * *
ethnicGiriama -19.02 15.21 -1.251 0.2137
ethnicJibana 2.789 33.5 0.08326 0.9338
ethnicKauma -23.8 42.53 -0.5597 0.5768
ethnicRabai 39.22 55.42 0.7077 0.4806
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
119 75.75 0.08768 0.03015
  2.5 % 97.5 %
(Intercept) 83.52 412.2
sickleHET -32.65 43.55
mcv_2010 -0.4319 3.714
hgb_2010 -30.75 -4.507
ethnicGiriama -49.16 11.12
ethnicJibana -63.59 69.17
ethnicKauma -108.1 60.47
ethnicRabai -70.6 149
  • sickle emmean SE df lower.CL upper.CL
  • NORM 172.8363 15.99202 111 141.1470 204.5255
  • HET 178.2860 21.54168 111 135.5997 220.9723
sickle n
NORM 113
HET 22

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

thal _ NORM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.33 2.495 5.343 4.993e-07 * * *
g6pd_202_rtpcrHET -2.284 0.6802 -3.358 0.001079 * *
g6pd_202_rtpcrHOM/HEMI -5.248 0.8348 -6.287 6.716e-09 * * *
age_at_collection_years_2010 -0.03981 0.08067 -0.4934 0.6227
mcv_2010 -0.06867 0.0337 -2.038 0.04398 *
ethnicGiriama -1.007 0.5179 -1.945 0.05429
ethnicJibana -0.05986 1.125 -0.0532 0.9577
ethnicKauma 0.4286 1.422 0.3014 0.7637
ethnicRabai 0.3786 1.903 0.199 0.8426
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
119 2.546 0.3731 0.3275
  2.5 % 97.5 %
(Intercept) 8.387 18.28
g6pd_202_rtpcrHET -3.632 -0.9361
g6pd_202_rtpcrHOM/HEMI -6.902 -3.593
age_at_collection_years_2010 -0.1997 0.1201
mcv_2010 -0.1355 -0.001884
ethnicGiriama -2.034 0.01887
ethnicJibana -2.289 2.17
ethnicKauma -2.39 3.247
ethnicRabai -3.392 4.149
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 7.626856 0.5352584 110 6.5660998 8.687613
  • HET 5.342873 0.8073750 110 3.7428450 6.942901
  • HOM/HEMI 2.379032 0.9152256 110 0.5652699 4.192795
g6pd_202_rtpcr n
NORM 100
HET 23
HOM/HEMI 12

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.9 2.969 5.019 1.997e-06 * * *
sickleHET 0.1798 0.7658 0.2347 0.8148
age_at_collection_years_2010 0.03821 0.09455 0.4042 0.6869
mcv_2010 -0.109 0.03947 -2.762 0.006728 * *
ethnicGiriama -0.7949 0.607 -1.309 0.1931
ethnicJibana 0.6022 1.329 0.4532 0.6513
ethnicKauma -0.1742 1.674 -0.1041 0.9173
ethnicRabai 1.438 2.246 0.6405 0.5232
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
119 3.017 0.1116 0.05553
  2.5 % 97.5 %
(Intercept) 9.016 20.78
sickleHET -1.338 1.697
age_at_collection_years_2010 -0.1491 0.2256
mcv_2010 -0.1872 -0.0308
ethnicGiriama -1.998 0.408
ethnicJibana -2.031 3.236
ethnicKauma -3.491 3.143
ethnicRabai -3.012 5.889
  • sickle emmean SE df lower.CL upper.CL
  • NORM 6.885353 0.644052 111 5.609121 8.161585
  • HET 7.065125 0.862482 111 5.356060 8.774191
sickle n
NORM 113
HET 22

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

thal _ HET ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 225.8 57.86 3.903 0.0001409 * * *
g6pd_202_rtpcrHET -45.19 11.83 -3.82 0.0001919 * * *
g6pd_202_rtpcrHOM/HEMI -139.5 14.39 -9.695 1.088e-17 * * *
mcv_2010 1.212 0.8439 1.436 0.1529
hgb_2010 -11.62 4.089 -2.842 0.005086 * *
ethnicDigo 60.88 39.15 1.555 0.122
ethnicDurum 21.19 54.82 0.3866 0.6996
ethnicGiriama -10 9.36 -1.069 0.2869
ethnicJibana -18.14 16.25 -1.116 0.2661
ethnicKauma -11.59 32.49 -0.3568 0.7217
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
166 54.08 0.4401 0.4078
  2.5 % 97.5 %
(Intercept) 111.5 340.1
g6pd_202_rtpcrHET -68.56 -21.83
g6pd_202_rtpcrHOM/HEMI -167.9 -111.1
mcv_2010 -0.4548 2.879
hgb_2010 -19.7 -3.543
ethnicDigo -16.46 138.2
ethnicDurum -87.09 129.5
ethnicGiriama -28.49 8.487
ethnicJibana -50.25 13.97
ethnicKauma -75.76 52.58
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 191.81925 13.08077 156 165.9810 217.65754
  • HET 146.62627 15.54775 156 115.9150 177.33755
  • HOM/HEMI 52.30165 17.33244 156 18.0651 86.53821
g6pd_202_rtpcr n
NORM 133
HET 36
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 399.8 69.72 5.734 4.893e-08 * * *
sickleHET -20.22 17.25 -1.172 0.2429
mcv_2010 -2.485 0.9429 -2.636 0.009234 * *
hgb_2010 -5.717 5.05 -1.132 0.2593
ethnicDigo 84.23 49.74 1.694 0.09234
ethnicDurum 45.31 68.93 0.6573 0.512
ethnicGiriama 6.381 11.61 0.5495 0.5834
ethnicJibana -0.7177 21.4 -0.03354 0.9733
ethnicKauma -43.14 40.66 -1.061 0.2903
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
166 68.13 0.1058 0.06024
  2.5 % 97.5 %
(Intercept) 262 537.5
sickleHET -54.28 13.85
mcv_2010 -4.348 -0.623
hgb_2010 -15.69 4.258
ethnicDigo -14.01 182.5
ethnicDurum -90.84 181.5
ethnicGiriama -16.55 29.32
ethnicJibana -42.98 41.54
ethnicKauma -123.5 37.17
  • sickle emmean SE df lower.CL upper.CL
  • NORM 166.6145 16.16128 157 134.6929 198.5361
  • HET 146.3989 21.28544 157 104.3562 188.4417
sickle n
NORM 167
HET 24

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

thal _ HET ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.05 2.323 6.479 1.148e-09 * * *
g6pd_202_rtpcrHET -1.593 0.4812 -3.31 0.001161 * *
g6pd_202_rtpcrHOM/HEMI -5.032 0.5879 -8.559 1.011e-14 * * *
age_at_collection_years_2010 -0.1252 0.06198 -2.021 0.04502 *
mcv_2010 -0.09169 0.03372 -2.719 0.007291 * *
ethnicDigo 3.038 1.617 1.879 0.06214
ethnicDurum 1.414 2.252 0.6279 0.531
ethnicGiriama -0.02755 0.4002 -0.06884 0.9452
ethnicJibana -0.4593 0.6673 -0.6884 0.4922
ethnicKauma -0.481 1.336 -0.3601 0.7193
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
166 2.226 0.4928 0.4635
  2.5 % 97.5 %
(Intercept) 10.46 19.64
g6pd_202_rtpcrHET -2.543 -0.6421
g6pd_202_rtpcrHOM/HEMI -6.193 -3.871
age_at_collection_years_2010 -0.2477 -0.002816
mcv_2010 -0.1583 -0.02508
ethnicDigo -0.1561 6.232
ethnicDurum -3.035 5.863
ethnicGiriama -0.8181 0.763
ethnicJibana -1.777 0.8587
ethnicKauma -3.12 2.158
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 7.855809 0.5386022 156 6.791914 8.919703
  • HET 6.263200 0.6355263 156 5.007853 7.518548
  • HOM/HEMI 2.823931 0.7098068 156 1.421858 4.226003
g6pd_202_rtpcr n
NORM 133
HET 36
HOM/HEMI 22

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.76 2.603 8.747 3.208e-15 * * *
sickleHET -0.657 0.6831 -0.9618 0.3376
age_at_collection_years_2010 -0.08536 0.07478 -1.142 0.2554
mcv_2010 -0.2155 0.03689 -5.842 2.882e-08 * * *
ethnicDigo 3.603 1.966 1.833 0.06874
ethnicDurum 2.099 2.718 0.7724 0.441
ethnicGiriama 0.4607 0.4788 0.9623 0.3374
ethnicJibana 0.06098 0.8424 0.07239 0.9424
ethnicKauma -1.555 1.604 -0.9696 0.3337
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
166 2.689 0.2553 0.2174
  2.5 % 97.5 %
(Intercept) 17.62 27.9
sickleHET -2.006 0.6922
age_at_collection_years_2010 -0.2331 0.06234
mcv_2010 -0.2884 -0.1426
ethnicDigo -0.2801 7.486
ethnicDurum -3.269 7.467
ethnicGiriama -0.485 1.406
ethnicJibana -1.603 1.725
ethnicKauma -4.722 1.613
  • sickle emmean SE df lower.CL upper.CL
  • NORM 6.910751 0.6374058 157 5.651755 8.169748
  • HET 6.253713 0.8405021 157 4.593563 7.913864
sickle n
NORM 167
HET 24

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

thal _ HOM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 416 87.3 4.765 1.125e-05 * * *
g6pd_202_rtpcrHET -58.57 18.9 -3.099 0.002883 * *
g6pd_202_rtpcrHOM/HEMI -146 19.27 -7.577 1.799e-10 * * *
mcv_2010 -0.6306 1.491 -0.4228 0.6738
hgb_2010 -16.15 7.166 -2.254 0.02765 *
ethnicDurum -70.39 53.16 -1.324 0.1902
ethnicGiriama -35.95 12.98 -2.77 0.007329 * *
ethnicJibana -40.26 30.88 -1.304 0.197
ethnicKambe -23.02 36.92 -0.6236 0.5351
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
73 48.34 0.5536 0.4977
  2.5 % 97.5 %
(Intercept) 241.6 590.4
g6pd_202_rtpcrHET -96.32 -20.82
g6pd_202_rtpcrHOM/HEMI -184.5 -107.5
mcv_2010 -3.61 2.349
hgb_2010 -30.46 -1.834
ethnicDurum -176.6 35.81
ethnicGiriama -61.88 -10.02
ethnicJibana -101.9 21.43
ethnicKambe -96.79 50.74
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 163.6202 14.33377 64 134.98523 192.25522
  • HET 105.0510 19.32096 64 66.45291 143.64903
  • HOM/HEMI 17.6106 22.49249 64 -27.32332 62.54451
g6pd_202_rtpcr n
NORM 69
HET 10
HOM/HEMI 8

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.5 115.9 4.188 8.653e-05 * * *
sickleHET 49.68 23.96 2.074 0.04207 *
mcv_2010 -4.727 1.955 -2.418 0.01842 *
hgb_2010 -1.128 9.444 -0.1195 0.9053
ethnicDurum -69.35 67.78 -1.023 0.3101
ethnicGiriama -24.21 17.36 -1.395 0.1679
ethnicJibana -16.65 41.4 -0.4021 0.6889
ethnicKambe -47.51 53.62 -0.886 0.3789
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
73 64.9 0.1828 0.09479
  2.5 % 97.5 %
(Intercept) 254 717.1
sickleHET 1.835 97.52
mcv_2010 -8.631 -0.8225
hgb_2010 -19.99 17.73
ethnicDurum -204.7 66.03
ethnicGiriama -58.87 10.46
ethnicJibana -99.34 66.04
ethnicKambe -154.6 59.58
  • sickle emmean SE df lower.CL upper.CL
  • NORM 127.5795 19.06852 65 89.4970 165.6619
  • HET 177.2593 25.15834 65 127.0146 227.5039
sickle n
NORM 74
HET 13

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

thal _ HOM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 25.96 4.161 6.239 3.941e-08 * * *
g6pd_202_rtpcrHET -2.296 0.9242 -2.485 0.0156 *
g6pd_202_rtpcrHOM/HEMI -6.068 0.9172 -6.617 8.713e-09 * * *
age_at_collection_years_2010 -0.02647 0.1006 -0.2631 0.7933
mcv_2010 -0.2495 0.06104 -4.088 0.0001239 * * *
ethnicDurum -2.579 2.552 -1.011 0.316
ethnicGiriama -1.726 0.6324 -2.729 0.008203 * *
ethnicJibana -1.672 1.511 -1.107 0.2726
ethnicKambe -1.756 1.756 -1 0.3211
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
73 2.354 0.5642 0.5098
  2.5 % 97.5 %
(Intercept) 17.65 34.27
g6pd_202_rtpcrHET -4.143 -0.4499
g6pd_202_rtpcrHOM/HEMI -7.901 -4.236
age_at_collection_years_2010 -0.2274 0.1745
mcv_2010 -0.3714 -0.1276
ethnicDurum -7.677 2.519
ethnicGiriama -2.989 -0.4622
ethnicJibana -4.691 1.347
ethnicKambe -5.264 1.752
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 7.615225 0.6989321 64 6.2189481 9.011503
  • HET 5.318999 0.9410477 64 3.4390399 7.198958
  • HOM/HEMI 1.546731 1.0791062 64 -0.6090315 3.702494
g6pd_202_rtpcr n
NORM 69
HET 10
HOM/HEMI 8

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 30.67 5.161 5.943 1.215e-07 * * *
sickleHET 2.216 1.094 2.026 0.04684 *
age_at_collection_years_2010 0.0365 0.1263 0.2891 0.7734
mcv_2010 -0.3538 0.07582 -4.667 1.578e-05 * * *
ethnicDurum -3.306 3.056 -1.082 0.2833
ethnicGiriama -1.11 0.7946 -1.397 0.1671
ethnicJibana -0.4071 1.904 -0.2138 0.8313
ethnicKambe -2.165 2.372 -0.9125 0.3649
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
73 2.971 0.2948 0.2188
  2.5 % 97.5 %
(Intercept) 20.36 40.98
sickleHET 0.03186 4.401
age_at_collection_years_2010 -0.2156 0.2886
mcv_2010 -0.5053 -0.2024
ethnicDurum -9.41 2.798
ethnicGiriama -2.697 0.4768
ethnicJibana -4.21 3.395
ethnicKambe -6.903 2.573
  • sickle emmean SE df lower.CL upper.CL
  • NORM 6.063284 0.8730807 65 4.319621 7.806946
  • HET 8.279586 1.1476792 65 5.987512 10.571660
sickle n
NORM 74
HET 13

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

thal _ HOM/HEMI ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
thal _ HOM/HEMI ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

sickle

u_rcc

sickle _ NORM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 241.1 36.56 6.595 1.958e-10 * * *
g6pd_202_rtpcrHET -45.38 9.14 -4.965 1.163e-06 * * *
g6pd_202_rtpcrHOM/HEMI -141.2 10.64 -13.26 7.811e-32 * * *
mcv_2010 1.804 0.4916 3.668 0.0002893 * * *
hgb_2010 -16.65 3.18 -5.235 3.135e-07 * * *
ethnicDigo 74.75 56.37 1.326 0.1858
ethnicDurum -24.22 39.86 -0.6075 0.544
ethnicGiriama -16.55 6.945 -2.384 0.01778 *
ethnicJibana 3.489 15.56 0.2242 0.8227
ethnicKauma -5.196 22.06 -0.2355 0.814
ethnicRabai 7.542 55.77 0.1352 0.8925
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
307 55.37 0.4149 0.3951
  2.5 % 97.5 %
(Intercept) 169.2 313.1
g6pd_202_rtpcrHET -63.37 -27.39
g6pd_202_rtpcrHOM/HEMI -162.1 -120.2
mcv_2010 0.836 2.771
hgb_2010 -22.9 -10.39
ethnicDigo -36.18 185.7
ethnicDurum -102.7 54.23
ethnicGiriama -30.22 -2.886
ethnicJibana -27.14 34.12
ethnicKauma -48.61 38.22
ethnicRabai -102.2 117.3
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 192.20502 13.47360 296 165.68882 218.72121
  • HET 146.82613 14.73115 296 117.83506 175.81720
  • HOM/HEMI 51.01747 16.40202 296 18.73812 83.29682
g6pd_202_rtpcr n
NORM 260
HET 58
HOM/HEMI 36

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 366 54.72 6.688 1.129e-10 * * *
thalHET -15.81 9.547 -1.656 0.09877
thalHOM -38.96 14.04 -2.774 0.005881 * *
mcv_2010 -1.068 0.7197 -1.484 0.1388
hgb_2010 -10.16 3.989 -2.546 0.01139 *
ethnicDigo 77.18 70.33 1.097 0.2733
ethnicDurum -12.99 49.95 -0.26 0.7951
ethnicGiriama -5.453 8.634 -0.6316 0.5281
ethnicJibana 24.11 19.39 1.244 0.2147
ethnicKauma -39.2 28.08 -1.396 0.1638
ethnicRabai 32.46 70.09 0.4632 0.6436
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
307 69.47 0.07918 0.04807
  2.5 % 97.5 %
(Intercept) 258.3 473.7
thalHET -34.6 2.978
thalHOM -66.59 -11.32
mcv_2010 -2.485 0.3481
hgb_2010 -18.01 -2.307
ethnicDigo -61.22 215.6
ethnicDurum -111.3 85.32
ethnicGiriama -22.44 11.54
ethnicJibana -14.05 62.27
ethnicKauma -94.46 16.06
ethnicRabai -105.5 170.4
  • thal emmean SE df lower.CL upper.CL
  • NORM 183.2146 17.59239 296 148.5926 217.8366
  • HET 167.4046 17.06330 296 133.8239 200.9854
  • HOM 144.2574 19.51114 296 105.8593 182.6556
thal n
NORM 113
HET 167
HOM 74

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

sickle _ NORM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.56 1.354 10.76 5.219e-23 * * *
g6pd_202_rtpcrHET -1.681 0.3885 -4.327 2.072e-05 * * *
g6pd_202_rtpcrHOM/HEMI -5.278 0.4504 -11.72 2.572e-26 * * *
age_at_collection_years_2010 -0.08531 0.04505 -1.894 0.05924
mcv_2010 -0.08625 0.01854 -4.651 4.976e-06 * * *
ethnicDigo 4.156 2.388 1.74 0.08287
ethnicDurum -0.3782 1.693 -0.2233 0.8234
ethnicGiriama -0.5303 0.3012 -1.76 0.07938
ethnicJibana 0.3967 0.662 0.5992 0.5495
ethnicKauma 0.06789 0.9395 0.07226 0.9424
ethnicRabai -0.1388 2.382 -0.0583 0.9535
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
307 2.358 0.4332 0.414
  2.5 % 97.5 %
(Intercept) 11.9 17.23
g6pd_202_rtpcrHET -2.446 -0.9165
g6pd_202_rtpcrHOM/HEMI -6.164 -4.392
age_at_collection_years_2010 -0.174 0.003347
mcv_2010 -0.1227 -0.04976
ethnicDigo -0.5442 8.856
ethnicDurum -3.711 2.954
ethnicGiriama -1.123 0.06254
ethnicJibana -0.9061 1.7
ethnicKauma -1.781 1.917
ethnicRabai -4.826 4.548
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 8.035471 0.5728501 296 6.908095 9.162846
  • HET 6.354346 0.6232595 296 5.127765 7.580927
  • HOM/HEMI 2.757326 0.6914449 296 1.396555 4.118097
g6pd_202_rtpcr n
NORM 260
HET 58
HOM/HEMI 36

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.24 2.104 10.09 8.903e-21 * * *
thalHET -0.6731 0.3921 -1.716 0.08713
thalHOM -1.722 0.588 -2.929 0.00366 * *
age_at_collection_years_2010 -0.03529 0.05597 -0.6306 0.5288
mcv_2010 -0.1871 0.02735 -6.84 4.543e-11 * * *
ethnicDigo 3.833 2.847 1.346 0.1792
ethnicDurum -0.2421 2.027 -0.1194 0.905
ethnicGiriama -0.171 0.3581 -0.4776 0.6333
ethnicJibana 1.115 0.789 1.413 0.1588
ethnicKauma -1.363 1.142 -1.193 0.2339
ethnicRabai 0.8045 2.86 0.2813 0.7787
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
307 2.828 0.1847 0.1572
  2.5 % 97.5 %
(Intercept) 17.09 25.38
thalHET -1.445 0.09866
thalHOM -2.88 -0.5653
age_at_collection_years_2010 -0.1454 0.07485
mcv_2010 -0.2409 -0.1333
ethnicDigo -1.77 9.435
ethnicDurum -4.231 3.747
ethnicGiriama -0.8758 0.5337
ethnicJibana -0.438 2.668
ethnicKauma -3.611 0.8856
ethnicRabai -4.823 6.432
  • thal emmean SE df lower.CL upper.CL
  • NORM 7.699965 0.7170104 296 6.288880 9.111049
  • HET 7.026887 0.6915251 296 5.665958 8.387816
  • HOM 5.977545 0.7941216 296 4.414705 7.540385
thal n
NORM 113
HET 167
HOM 74

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

sickle _ HET ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 262 73.6 3.559 0.0009566 * * *
g6pd_202_rtpcrHET -107.9 20.08 -5.373 3.351e-06 * * *
g6pd_202_rtpcrHOM/HEMI -159.2 21.29 -7.478 3.515e-09 * * *
mcv_2010 2.337 0.9859 2.371 0.02253 *
hgb_2010 -18.41 6.349 -2.9 0.00597 * *
ethnicDigo -2.176 49.19 -0.04423 0.9649
ethnicGiriama -38.75 15.5 -2.501 0.01647 *
ethnicJibana -59.35 21.61 -2.746 0.008912 * *
ethnicKambe -41.96 36.82 -1.14 0.2611
ethnicRabai 8.68 49.48 0.1754 0.8616
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
51 47.51 0.6987 0.6326
  2.5 % 97.5 %
(Intercept) 113.3 410.6
g6pd_202_rtpcrHET -148.5 -67.36
g6pd_202_rtpcrHOM/HEMI -202.2 -116.2
mcv_2010 0.3463 4.328
hgb_2010 -31.23 -5.591
ethnicDigo -101.5 97.16
ethnicGiriama -70.05 -7.461
ethnicJibana -103 -15.7
ethnicKambe -116.3 32.4
ethnicRabai -91.25 108.6
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 204.97211 13.80872 41 177.084844 232.8594
  • HET 97.05074 21.47876 41 53.673516 140.4280
  • HOM/HEMI 45.77694 23.59093 41 -1.865912 93.4198
g6pd_202_rtpcr n
NORM 42
HET 11
HOM/HEMI 6

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 217.7 131.8 1.653 0.1061
thalHET -10.41 28.66 -0.3632 0.7183
thalHOM 14.67 34.4 0.4266 0.6719
mcv_2010 1.273 1.708 0.7453 0.4603
hgb_2010 -11.05 10.07 -1.097 0.279
ethnicDigo 42.55 83.6 0.509 0.6135
ethnicGiriama -29.02 26.85 -1.081 0.2861
ethnicJibana -80.39 37.77 -2.128 0.03938 *
ethnicKambe -29.73 64.48 -0.461 0.6472
ethnicRabai 43.24 82.82 0.5221 0.6044
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 3, 4)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
51 78.35 0.1804 0.0004776
  2.5 % 97.5 %
(Intercept) -48.36 483.8
thalHET -68.3 47.48
thalHOM -54.79 84.14
mcv_2010 -2.177 4.723
hgb_2010 -31.38 9.289
ethnicDigo -126.3 211.4
ethnicGiriama -83.24 25.2
ethnicJibana -156.7 -4.105
ethnicKambe -159.9 100.5
ethnicRabai -124 210.5
  • thal emmean SE df lower.CL upper.CL
  • NORM 178.5513 26.91957 41 124.1862 232.9165
  • HET 168.1396 27.20583 41 113.1963 223.0829
  • HOM 193.2261 32.85289 41 126.8784 259.5739
thal n
NORM 22
HET 24
HOM 13

END OF ENZYME ACTIVITY_______hgb__________END OF ENZYME ACTIVITY_______________hgb

u_ghb3

sickle _ HET ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.82 3.036 5.539 1.952e-06 * * *
g6pd_202_rtpcrHET -3.807 0.958 -3.974 0.0002797 * * *
g6pd_202_rtpcrHOM/HEMI -6.152 1.013 -6.075 3.382e-07 * * *
age_at_collection_years_2010 0.03552 0.1182 0.3006 0.7652
mcv_2010 -0.1052 0.04452 -2.363 0.02293 *
ethnicDigo -0.8956 2.391 -0.3746 0.7099
ethnicGiriama -1.645 0.7463 -2.204 0.0332 *
ethnicJibana -2.111 1.025 -2.06 0.04583 *
ethnicKambe -2.871 1.719 -1.67 0.1026
ethnicRabai -0.0149 2.434 -0.006119 0.9951
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 2, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
51 2.252 0.647 0.5696
  2.5 % 97.5 %
(Intercept) 10.69 22.95
g6pd_202_rtpcrHET -5.742 -1.872
g6pd_202_rtpcrHOM/HEMI -8.197 -4.107
age_at_collection_years_2010 -0.2031 0.2741
mcv_2010 -0.1951 -0.01531
ethnicDigo -5.724 3.933
ethnicGiriama -3.152 -0.1376
ethnicJibana -4.181 -0.04097
ethnicKambe -6.343 0.6018
ethnicRabai -4.931 4.902
  • g6pd_202_rtpcr emmean SE df lower.CL upper.CL
  • NORM 8.109038 0.6459338 41 6.804548 9.413527
  • HET 4.302008 1.0371715 41 2.207397 6.396618
  • HOM/HEMI 1.957373 1.1147385 41 -0.293887 4.208633
g6pd_202_rtpcr n
NORM 42
HET 11
HOM/HEMI 6

  Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.22 4.983 3.657 0.0007197 * * *
thalHET -0.7394 1.209 -0.6115 0.5443
thalHOM 0.107 1.437 0.07446 0.941
age_at_collection_years_2010 0.2024 0.1663 1.217 0.2307
mcv_2010 -0.1583 0.06999 -2.262 0.02907 *
ethnicDigo 0.2429 3.507 0.06927 0.9451
ethnicGiriama -1.397 1.115 -1.252 0.2176
ethnicJibana -2.794 1.554 -1.798 0.07956
ethnicKambe -2.188 2.61 -0.8382 0.4068
ethnicRabai 1.899 3.486 0.5448 0.5889
Fitting linear model: as.formula(paste(ed[m], “~”, paste(colnames(b[-c(length(b), 1, 4, 6)]), collapse = “+”)))
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
51 3.232 0.2734 0.1139
  2.5 % 97.5 %
(Intercept) 8.159 28.29
thalHET -3.182 1.703
thalHOM -2.794 3.008
age_at_collection_years_2010 -0.1335 0.5382
mcv_2010 -0.2996 -0.01696
ethnicDigo -6.84 7.325
ethnicGiriama -3.649 0.856
ethnicJibana -5.932 0.3444
ethnicKambe -7.459 3.083
ethnicRabai -5.141 8.939
  • thal emmean SE df lower.CL upper.CL
  • NORM 7.443893 1.113924 41 5.194278 9.693508
  • HET 6.704445 1.123098 41 4.436304 8.972587
  • HOM 7.550867 1.352847 41 4.818739 10.282997
thal n
NORM 22
HET 24
HOM 13

END OF ENZYME ACTIVITY_______END OF ENZYME ACTIVITY__________END OF ENZYME ACTIVITY_______________END OF ENZYME ACTIVITY

u_rcc

sickle _ HOM ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
sickle _ HOM ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

u_rcc

sickle _ HOM/HEMI ENZYME ACTIVITY_______hgb__________ENZYME ACTIVITY________________hgb ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels #### u_ghb3
sickle _ HOM/HEMI ENZYME ACTIVITY_______rcc__________ENZYME ACTIVITY________________rcc ERROR : contrasts can be applied only to factors with 2 or more levels ERROR : contrasts can be applied only to factors with 2 or more levels

g6pd202 _ NORM ________________________________________________________________
g6pd202 _ HET ________________________________________________________________
g6pd202 _ HOM ________________________________________________________________
g6pd202 _ HOM/HEMI ________________________________________________________________

##wambua G6PDd heterozygotes; not accounting for sex as G6PDd is x-linked {.tabset}

##wambua G6PDd heterozygotes; not accounting for sex as G6PDd is x-linked : G6PD activity